-
1Academic Journal
المؤلفون: N. A. Karelskaya, I. S. Gruzdev, V. Yu. Raguzina, G. G. Karmazanovsky, Н. А. Карельская, И. С. Груздев, В. Ю. Рагузина, Г. Г. Кармазановский
المصدر: Diagnostic radiology and radiotherapy; Том 14, № 4 (2023); 7-18 ; Лучевая диагностика и терапия; Том 14, № 4 (2023); 7-18 ; 2079-5343
مصطلحات موضوعية: почечно-клеточный рак, MRI, Texture analysis, renal cell carcinoma, МРТ, текстурный анализ
وصف الملف: application/pdf
Relation: https://radiag.bmoc-spb.ru/jour/article/view/934/617; Moch H., Cubilla A.L., Humphrey P.A. et al. Ulbright. The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs — Part A: Renal, Penile, and Testicular Tumours // Eur. Urol. 2016. Vol. 70, No. 1. Р. 93–105. doi:10.1016/J.EURURO.2016.02.029.; Halefoglu A.M., Ozagari A.A. Tumor grade estimation of clear cell and papillary renal cell carcinomas using contrast-enhanced MDCT and FSE T2 weighted MR imaging: radiology-pathology correlation // Radiol. Med. 2021. Vol. 126, No. 9. Р. 1139–1148. doi:10.1007/S11547-021-01350-Y.; Miles K.A., Ganeshan B., Hayball M.P. CT texture analysis using the filtration-histogram method: what do the measurements mean? // Cancer Imaging. 2013. Vol. 13, No. 3. Р. 400–406. doi:10.1102/1470-7330.2013.9045.; Nioche C., Orlhac F., Boughdad S. et al. Lifex: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity // Cancer Res. 2018. Vol. 78, No. 16. Р. 4786–4789. doi:10.1158/0008-5472.CAN-18-0125.; Schieda N., Lim R.S., Krishna S. et al. Diagnostic Accuracy of Unenhanced CT Analysis to Differentiate Low-Grade From High-Grade Chromophobe Renal Cell Carcinoma // AJR. Am. J. Roentgenol. 2018. Vol. 210, No. 5. Р. 1079–1087. doi:10.2214/AJR.17.18874.; Bektas C.T., Kocak B., Yardimci A.H. et al. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade // Eur. Radiol. 2019. Vol. 29, No. 3. Р. 1153–1163. doi:10.1007/S00330-018-5698-2.; Cornelis F., Tricaud E., Lasserre A.S. et al. Multiparametric magnetic resonance imaging for the differentiation of low- and high-grade clear cell renal carcinoma // Eur. Radiol. 2015. Vol. 25, No. 1. Р. 24–31. doi:10.1007/S00330-014-3380-X.; Oh S., Sung D.J., Yang K.S. et al. Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma // Acta Radiologica. 2016. Vol. 58, No. 3. Р. 376–384. doi:10.1177/0284185116649795.; Sun R., Zhao S., Jiang H. et al. Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis // Biomed. Res. Int. 2021. Vol. 1. Р. 1–11. doi:10.1155/2021/1821876.; Muglia V.F., Prando A. Renal cell carcinoma: histological classification and correlation with imaging findings // Radiol. Bras. 2015. Vol. 48, No. 3. Р. 166–174. doi:10.1590/0100-3984.2013.1927.; Zhu Y.H., Wang X., Zhang J., Chen Y.H. et al. Low enhancement on multiphase contrast-enhanced CT images: an independent predictor of the presence of high tumor grade of clear cell renal cell carcinoma // AJR. Am. J. Roentgenol. 2014. Vol. 203, No. 3. Р. W295–W300. doi:10.2214/AJR.13.12297.; Goyal A., Sharma R., Bhalla A.S. et al. Diffusion-weighted MRI in renal cell carcinoma: A surrogate marker for predicting nuclear grade and histological subtype // Acta radiol. 2012. Vol. 53, No. 3. Р. 349–358. doi:10.1258/AR.2011.110415/ASSET/IMAGES/LARGE/10.1258_AR.2011.110415-FIG2.JPEG.; Yi X., Xiao Q., Zeng F. et al. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma // Front. Oncol. 2020. Vol. 10. Р. 570396–570396. doi:10.3389/FONC.2020.570396.; Kim N.Y., Lubner M.G., Nystrom J.T. et al. Utility of CT Texture Analysis in Differentiating Low-Attenuation Renal Cell Carcinoma From Cysts: A Bi-Institutional Retrospective Study // American Journal of Roentgenology. 2019. Vol. 213, No. 6. Р. 1259–1266. doi:10.2214/AJR.19.21182.; Yu H.S., Scalera J., Khalid M. et al. Texture analysis as a radiomic marker for differentiating renal tumors // Abdom. Radiol. 2017. Vol. 42, No. 10. Р. 2470–2478. doi:10.1007/S00261-017-1144-1/TABLES/4.; Ding J., Xing Z., Jiang Z. et al. CT-based radiomic model predicts high grade of clear cell renal cell carcinoma // Eur. J. Radiol. Vol. 103. Р. 51–56. doi:10.1016/J.EJRAD.2018.04.013.; Paschall A.K., Mirmomen S.M., Symons R. et al. Differentiating papillary type I RCC from clear cell RCC and oncocytoma: application of whole-lesion volumetric ADC measurement // Abdom. Radiol. (New York). 2018. Vol. 43, No. 9. Р. 2424–2430. doi:10.1007/S00261-017-1453-4.; Shen L., Zhou L., Liu X. et al. Comparison of biexponential and monoexponential DWI in evaluation of Fuhrman grading of clear cell renal cell carcinoma // Diagnostic Interv. Radiol. 2017. Vol. 23, No. 2, p. 100. doi:10.5152/DIR.2016.15519.; Villavicencio C.P., McCarthy R.J., Miller F.H. Can diffusion-weighted magnetic resonance imaging of clear cell renal carcinoma predict low from high nuclear grade tumors // Abdom. Radiol. (New York). 2017. Vol. 42, No. 4. Р. 1241–1249. doi:10.1007/S00261-016-0981-7.; Mytsyk Y., Dutka I., Borys Y. et al. Renal cell carcinoma: applicability of the apparent coefficient of the diffusion-weighted estimated by MRI for improving their differential diagnosis, histologic subtyping, and differentiation grade // Int. Urol. Nephrol. 2017. Vol. 49, No. 2. Р. 215–224. doi:10.1007/S11255-016-1460-3.; Adams L.C., Bressem K.K., Jurmeister P. et al. Use of quantitative T2 mapping for the assessment of renal cell carcinomas: First results // Cancer Imaging. 2019. Vol. 19, No. 1. Р. 1–11. doi:10.1186/S40644-019-0222-8/FIGURES/5.; Zhang Y.D., Wu C.J., Wang Q. et al. Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma // AJR. Am. J. Roentgenol. 2015. Vol. 205, No. 2. Р. W193–W201. doi:10.2214/AJR.14.13802.; Moran K., Abreu-Gomez J., Krishn S. et al. Can MRI be used to diagnose histologic grade in T1a (; Kierans A.S., Rusinek H., Lee A. et al. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma // AJR. Am. J. Roentgenol. 2014. Vol. 203, No. 6. Р. W637–W644. doi:10.2214/AJR.14.12570.; Jiang Y., Li W., Huang C. et al. A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma // Front. Oncol. 2020. Vol. 10. Р. 592. doi:10.3389/FONC.2020.00592/BIBTEX.; Tordjman M., Mali R., Madelin G. et al. Diagnostic test accuracy of ADC values for identification of clear cell renal cell carcinoma: systematic review and meta-analysis // Eur. Radiol. 2020. Vol. 30, No. 7. Р. 4023–4038. doi:10.1007/S00330-020-06740-W.; Vendrami C.L., Velichko Y.S., Miller F.H. et al. Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis // AJR. Am. J. Roentgenol. 2018. Vol. 211, No. 6. Р. 1234–1245. doi:10.2214/AJR.17.19213.; Kocak B., Ates E., Durmaz E.S. et al. Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas // Eur. Radiol. 2019. Vol. 29, No. 9. Р. 4765–4775. doi:10.1007/S00330-019-6003-8/TABLES/5.; Lin F., Cui E.M., Lei Y. et al. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma // Abdom. Radiol. 2019. Vol. 44, No. 7. Р. 2528–2534. doi:10.1007/S00261-019-01992-7/TABLES/2.; Cui E., Li Z., Ma C. et al. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics // Eur. Radiol. 2020. Vol. 30, No. 5. Р. 2912–2921. doi:10.1007/S00330-019-06601-1/FIGURES/3.; Espinasse M., Pitre-Champagnat S., Charmettant B. et al. CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review // Diagnostics 2020. Vol. 10, No. 5. Р. 258. doi.org/10.3390/diagnostics10050258.; Buvat I., Orlhac F., Soussan M. Tumor Texture Analysis in PET: Where Do We Stand? // J. Nucl. Med. 2015. Vol. 56, No. 11. Р. 1642–1644. doi:10.2967/JNUMED.115.163469.; Kocak B., Yardimci A.H., Bektas C.T. et al. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation // Eur. J. Radiol. 2018. Vol. 107. Р. 149–157. doi:10.1016/J.EJRAD.2018.08.014.; Lee H. S., Hong H., Jung D.C. et al. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification // Med. Phys. 2017. Vol. 44, No. 7. Р. 3604–3614, doi:10.1002/MP.12258.; Leng S., Takahashi N., Gomez Cardona D. et al. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT // Abdom. Radiol. 2017. Vol. 42, No. 5. Р. 1485–1492. doi:10.1007/S00261-016-1014-2/FIGURES/5.; Nazari M., Shiri I., Hajianfar G. et al. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning // Radiol. Medica. 2020. Vol. 125, No. 8. Р. 754–762. doi:10.1007/S11547-020-01169-Z/TABLES/4.; Sun J., Pan L., Zha T. et al. The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma // Acta radiol. 2021. Vol. 62, No. 8. Р. 1104–1111. doi:10.1177/0284185120951964/ASSET/IMAGES/LARGE/10.1177_0284185120951964-FIG2.JPEG.; Nguyen K., Schieda N., James N. et al. Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase–enhanced CT images // Eur. Radiol. 2021. Vol. 31, No. 3. Р. 1676–1686. doi:10.1007/S00330-020-07233-6/TABLES/5.; Lubner M.G., Stabo N., Abel E.J. et al. CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes // Am. J. Roentgenol. 2016. Vol. 207, No. 1. Р. 96–105. doi:10.2214/AJR.15.15451.; Lai S., Sun L., Wu J. et al. Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma // Cancer Manag. Res. 2021. Vol. 13. Р. 999. doi:10.2147/CMAR.S290327.; Deng Y., Soule E., Samuel A. et al. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade // Eur. Radiol. 2019. Vol. 29, No. 12. Р. 6922–6929. doi:10.1007/S00330-019-06260-2/FIGURES/4.; Feng Z., Shen Q., Li Y. et al. CT texture analysis: A potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma // Cancer Imaging. 2019. Vol. 19, No. 1. Р. 1–7. doi:10.1186/S40644-019-0195-7/FIGURES/2.; Haji-Momenian S., Lin Z., Patel B. et al. Texture analysis and machine learning algorithms accurately predict histologic grade in small (; Scrima A.T., Lubner M.G., Abel E.J. et al. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers // Abdom. Radiol. 2019. Vol. 44, No. 6. Р. 1999–2008. doi:10.1007/S00261-018-1649-2/FIGURES/3.; Shu J., Tang Y., Cui J. et al. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade // Eur. J. Radiol. 2018. Vol. 109. Р. 8–12. doi:10.1016/J.EJRAD.2018.10.005.; Wu K., Wu P., Yang K. et al. A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images // Eur. Radiol. 2022. Vol. 32, No. 4. Р. 2255–2265. doi:10.1007/S00330-021-08353-3/FIGURES/4.; Gao R., Qin H., Lin P. et al. Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma // Front. Oncol. 2021. Vol. 11. Р. 2347. doi:10.3389/FONC.2021.613668/BIBTEX.; Demirjian N.L., Varghese B.A., Cen S.Y. et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma // Eur. Radiol. 2022. Vol. 32, No. 4. Р. 2552–2563. doi:10.1007/S00330-021-08344-4/TABLES/3.; Zhang H., Yin F., Chen M. et al. Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma // Front. Oncol. 2022. Vol. 11. doi:10.3389/FONC.2021.742547.; Shehata M., Alksas A., Abouelkheir R.T. et al. A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors // Sensors. 2021. Vol. 21, No. 14. Р. 4928. doi:10.3390/S21144928.; Goyal A., Razik A., Kandasamy D. et al. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study // Abdom. Radiol. 2019. Vol. 44, No. 10. Р. 3336–3349. doi:10.1007/S00261-019-02122-Z/FIGURES/4.; Wang W., Cao K.M., Jin S.M. et al. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis // Eur. Radiol. 2020. Vol. 30, No. 10. Р. 5738–5747. doi:10.1007/S00330-020-06896-5/FIGURES/2.; Razik A., Goyal A., Sharma R. et al. MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma // Br. J. Radiol. 2020. Vol. 93, No. 1114. doi:10.1259/BJR.20200569.; https://radiag.bmoc-spb.ru/jour/article/view/934
-
2Academic Journal
المؤلفون: A. S. Shershever, E. A. Daineko, S. N. Soloveva, E. E. Surova, E. F. Askarova, А. С. Шершевер, Е. А. Дайнеко, С. Н. Соловьева, Е. Е. Сурова, Е. Ф. Аскарова
المصدر: Diagnostic radiology and radiotherapy; Том 15, № 2 (2024); 35-44 ; Лучевая диагностика и терапия; Том 15, № 2 (2024); 35-44 ; 2079-5343
مصطلحات موضوعية: МРТ, differentiation, modeling, texture analysis, classification, MRI, дифференциация, моделирование, текстурный анализ, классификация
وصف الملف: application/pdf
Relation: https://radiag.bmoc-spb.ru/jour/article/view/1003/644; Mladenovsk M., Valkov I., Ovcharov M., Vasilev N., Duhlenski I. High Grade Glioma Surgery — Clinical Aspects and Prognosis // Folia Med. (Plovdiv). 2021. Vol. 63, No. 1. P. 35–41. doi:10.3897/folmed.63.e55255.; Cancer Stat Facts: Brain and Other Nervous System Cancer // National Cancer Institute Surveillance, Epidemiology and End Results Program. 2023.; Louis D.N., Perry A., Reifenberger G., von Deimling A., Figarella-Branger D., Cavenee W.K., Ohgaki H., Wiestler O.D., Kleihues P., Ellison D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary // Acta neuropathologica. 2016. Vol. 131, No. 6. P. 803–820. doi:10.1007/s00401-016-1545-1.; Спивак М.Д. Современные возможности основных методов лечения мультиформной глиобластомы // Молодой ученый. 2018. № 17 (203). С. 120–123.; Грибанова Т.Г., Фокин В.А., Мартынов Б.В., Труфанов Г.Е., Малаховский В.Н., Серебрякова С.В. Сопоставление различных методов нейровизуализации в дифференциальной диагностике рецидива злокачественных опухолей головного мозга и лучевого некроза // Вестник Санкт-Петербургского университета. Медицина. 2016. № 3. С. 56–63.; Kim J.M., Miller J.A., Kotecha R., Xiao R., Juloori A., Ward M.C., Ahluwalia M.S., Mohammadi A.M., Peereboom D.M., Murphy E.S., Suh J.H., Barnett G.H., Vogelbaum M.A., Angelov L., Stevens G.H., Chao S.T. The risk of radiation necrosis following stereotactic radiosurgery with concurrent systemic therapies // Journal of neurooncology. 2017. Vol. 133, No. 2. P. 357–368. doi:10.1007/s11060-017-2442-8.; Gahramanov S., Muldoon L.L., Varallyay C.G., Li X., Kraemer D.F., Fu R., Hamilton B.E., Rooney W.D., Neuwelt E.A. Pseudoprogression of glioblastoma after chemo- and radiation therapy: diagnosis by using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging with ferumoxytol versus gadoteridol and correlation with survival // Radiology. 2013. No. 266 (3). P. 842–852. doi:10.1148/radiol.12111472.; Kong D.S., Kim S.T., Kim E.H., Lim D.H., Kim W.S., Suh Y.L., Lee J.I., Park K., Kim J.H., Nam D.H. Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status // AJNR. American journal of neuroradiology. 2011. Vol. 32, No. 2. P. 382–387. doi:10.3174/ajnr.A2286.; Трофимова Т.Н. Нейрорадиология: оценка эффективности хирургии и комбинированной терапии глиом // Практическая онкология. 2016. Т. 17, № 1. С. 32–40.; Hotta M., Minamimoto R., Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier // Scientific Reports. 2019. No. 9 (1). doi:10.1038/s41598-019-52279-2.; He Q., Zhang L., Zhang B., Shi X., Yi C., Zhang X. Diagnostic accuracy of 13N-ammonia PET, 11C-methionine PET and 18F-fluorodeoxyglucose PET: a comparative study in patients with suspected cerebral glioma // BMC Cancer. 2019. Vol. 19 (1). doi:10.1186/s12885-019-5560-1. PMID 30961564; PMCID PMC6454631.; Jung J.H., Ahn B.C. Current Radiopharmaceuticals for Positron Emission Tomography of Brain Tumors // Brain tumor research and treatment. 2018. Vol. 6 (2). P. 47–53. doi:10.14791/btrt.2018.6.e13.; Соловьева С.Н., Уросова В.С. Разработка модели автоматического определения границ глиомы головного мозга, на основе комплексного метода обработки МРТ- и КТ-изображения // Современные наукоемкие технологии. 2018. № 5. С. 83–88.; Jog A., Roy S., Carass A., Prince J.L. Magnetic resonance image synthesis through patch regression // Proceedings. IEEE International Symposium on Biomedical Imaging. 2013. Vol. 2013. P. 350–353. doi:10.1109/ISBI.2013.6556484.; Sonavane R., Sonar P. Classification and segmentation of brain tumor using Adaboost classifier // 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). P. 396–403. doi:10.1109/ICGTSPICC.2016.7955334.; Dohm A.E., Nickles T.M., Miller C.E., Bowers H.J., Miga M.I., Attia A., Chan M.D., Weis J.A. Clinical assessment of a biophysical model for distinguishing tumor progression from radiation necrosis // Medical physics. 2021. Vol. 48, No. 7. P. 3852–3859. doi:10.1002/mp.14999. Epub 2021 Jun 16.; Gao Y., Xiao X., Han B., Li G., Ning X., Wang D., Cai W., Kikinis R., Berkovsky S., Di Ieva A., Zhang L., Ji N., Liu S. Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation // JMIR medical informatics. 2020. Vol. 8, No. 11. doi:10.2196/19805.; Nichelli L., Casagranda S. Current emerging MRI tools for radionecrosis and pseudoprogression diagnosis // Current opinion in oncology. 2021. Vol. 33, No. 6. P. 597–607. doi:10.1097/CCO.0000000000000793.; https://radiag.bmoc-spb.ru/jour/article/view/1003
-
3Academic Journal
المؤلفون: A. A. Kovalenko, V. S. Petrovichev, O. V. Kryuchkova, Z. A. Kovalenko, D. P. Ananev, D. A. Matveev, R. V. Petrov, А. А. Коваленко, В. С. Петровичев, О. В. Крючкова, З. А. Коваленко, Д. П. Ананьев, Д. А. Матвеев, Р. В. Петров
المصدر: Diagnostic radiology and radiotherapy; Том 15, № 2 (2024); 53-64 ; Лучевая диагностика и терапия; Том 15, № 2 (2024); 53-64 ; 2079-5343
مصطلحات موضوعية: компьютерная томография, cystic tumor, radiomics, texture analysis, computed tomography, кистозная опухоль, радиомика, текстурный анализ
وصف الملف: application/pdf
Relation: https://radiag.bmoc-spb.ru/jour/article/view/1005/646; Chu L.C., Park S., Soleimani S. et al. Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists // Abdom. Radiol. (NY). 2022. Vol. 47, No. 12. Р. 4139–4150. doi:10.1007/s00261-022-03663-6.; Ozaki K., Ikeno H., Kaizaki Y. et al. Pearls and pitfalls of imaging features of pancreatic cystic lesions: a case-based approach with imaging-pathologic correlation // Jpn. J. Radiol. 2021. Vol. 39, No. 2. Р. 118–142. doi:10.1007/s11604-020-01032-1.; Kloth C, Haggenmüller B, Beck A et al. Diagnostic, Structured Classification and Therapeutic Approach in Cystic Pancreatic Lesions: Systematic Findings with Regard to the European Guidelines // Diagnostics (Basel). 2023. Vol. 13, No. 3. Р. 454. doi:10.3390/diagnostics13030454.; De Pretis N., Mukewar S., Aryal-Khanal A., Bi Y., Takahashi N., Chari S. Pancreatic cysts: Diagnostic accuracy and risk of inappropriate resections // Pancreatology. 2017. Vol. 17, No. 2. Р. 267–272. doi:10.1016/j.pan.2017.01.002.; Liu H., Cui Y., Shao J., Shao Z., Su F., Li Y. The diagnostic role of CT, MRI/MRCP, PET/CT, EUS and DWI in the differentiation of benign and malignant IPMN: A meta-analysis // Clin Imaging. 2021. Vol. 72. Р. 183–193. doi:10.1016/j.clinimag.2020.11.018.; Zhu S., Wang W.T., Shang X.S. et al. Difference analysis in prevalence of incidental pancreatic cystic lesions between computed tomography and magnetic resonance imaging // BMC Med. Imaging. 2019. Vol. 19, No. 1. Р. 43–52. doi:10.1186/s12880-019-0341-5.; Udare A., Agarwal M., Alabousi M. et al. Diagnostic Accuracy of MRI for Differentiation of Benign and Malignant Pancreatic Cystic Lesions Compared to CT and Endoscopic Ultrasound: Systematic Review and Meta-analysis // J. Magn. Reson. Imaging. 2021. Vol. 54, No. 4. Р. 1126–1137. doi:10.1002/jmri.27606.; Ishigami K., Nishie A., Mochidome N. et al. Mucinous nonneoplastic cyst of the pancreas: CT and MRI appearances // Abdom. Radiol (NY). 2017. Vol. 42, No. 12. Р. 2827–2834. doi:10.1007/s00261-017-1204-6.; Kovalenko A., Karmazanovsky G. Preconditions for radiomics-based approach in differential diagnosis of pancreatic cystic lesions: critical evaluation of meta-analyses and international guidelines // Diagnostic radiology and radiotherapy. 2023. Vol. 14, No 3. Р. 27–38. https://doi.org/10.22328/2079-5343-2023-14-3-27-38.; Tanaka M., Fernández-Del Castillo C., Kamisawa T. et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas // Pancreatology. 2017. Vol. 17, No. 5. Р. 738–753. doi:10.1016/j.pan.2017.07.007.; Vege S.S., Ziring B., Jain R., Moayyedi P. Clinical Guidelines Committee; American Gastroenterology Association. American gastroenterological association institute guideline on the diagnosis and management of asymptomatic neoplastic pancreatic cysts // Gastroenterology. 2015. Vol. 148, No. 4. Р. 819–822. doi:10.1053/j.gastro.2015.01.015.; European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms // Gut. 2018. Vol. 67, No. 5. Р. 789– 804. doi:10.1136/gutjnl-2018-316027.; Megibow A.J., Baker M.E., Morgan D.E. et al. Management of Incidental Pancreatic Cysts: A White Paper of the ACR Incidental Findings Committee // J. Am. Coll Radiol. 2017. Vol. 14, No. 7. Р. 911–923. doi:10.1016/j.jacr.2017.03.010.; Dunn D.P., Brook O.R., Brook A. et al. Measurement of pancreatic cystic lesions on magnetic resonance imaging: efficacy of standards in reducing inter-observer variability // Abdom. Radiol. (NY). 2016. Vol. 41, No. 3. Р. 500–507. doi:10.1007/s00261-015-0588-4.; Boos J., Brook A., Chingkoe C.M. et al. MDCT vs. MRI for incidental pancreatic cysts: measurement variability and impact on clinical management // Abdom. Radiol. (NY). 2017. Vol. 42, No. 2, Р. 521–530. doi:10.1007/s00261-016-0883-8.; Takakura K., Torisu Y., Kinoshita Y. et al. An Appraisal of Current Guidelines for Managing Malignancy in Pancreatic Intraductal Papillary Mucinous Neoplasm // JOP. 2018. Vol. 19, No. 4. Р. 178–182.; Elta G.H., Enestvedt B.K., Sauer B.G., Lennon A.M. ACG Clinical Guideline: Diagnosis and Management of Pancreatic Cysts // Am. J. Gastroenterol. 2018. Vol. 113, No. 4. Р. 464–479. doi:10.1038/ajg.2018.14.; Boot C. A review of pancreatic cyst fluid analysis in the differential diagnosis of pancreatic cyst lesions // Ann Clin Biochem. 2014. Vol. 51, No. 2. Р. 151–166. doi:10.1177/0004563213503819.; Abdelkader A., Hunt B., Hartley C.P., Panarelli N.C., Giorgadze T. Cystic Lesions of the Pancreas: Differential Diagnosis and Cytologic-Histologic Correlation // Arch Pathol Lab Med. 2020. Vol. 144, No. 1. Р. 47–61. doi:10.5858/arpa.2019-0308-RA.; Rizzo S., Botta F., Raimondi S. et al. Radiomics: the facts and the challenges of image analysis // Eur Radiol. Exp. 2018; Vol. 2, No. 1. doi:10.1186/s41747-018-0068-z.; Dalal V., Carmicheal J., Dhaliwal A., Jain M., Kaur S., Batra S.K. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action // Cancer Lett. 2020. Vol. 469. Р. 228–237. doi:10.1016/j.canlet.2019.10.023.; Mamone G., Barresi L., Tropea A., Di Piazza A., Miraglia R. MRI of mucinous pancreatic cystic lesions: a new updated morphological approach for the differential diagnosis // Updates Surg. 2020. Vol. 72, No. 3. Р. 617–637. doi:10.1007/s13304-020-00800-y.; Habashi S., Draganov P.V. Pancreatic pseudocyst // World J. Gastroenterol. 2009. Vol. 15, No. 1. Р. 38–47. doi:10.3748/wjg.15.38.; Amico E.C., Alves J.R., de Araújo Lima Liguori A., Sousa R.L. Serous Pancreatic Cystadenoma with Compression of Wirsung’s Duct // J. Gastrointest. Surg. 2019. Vol. 23, No. 1. Р. 176–178. doi:10.1007/s11605-018-3794-x.; Hanania A.N., Bantis L.E., Feng Z. et al. Quantitative imaging to evaluate malignant potential of IPMNs // Oncotarget. 2016. Vol. 7, No. 52. Р. 85776–85784. doi:10.18632/oncotarget.11769.; Yang J., Guo X., Ou X., Zhang W., Ma X. Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning // Front Oncol. 2019. Vol. 9, No. 494. doi:10.3389/fonc.2019.00494.; Xie H., Ma S., Guo X., Zhang X., Wang X. Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model // Eur. J. Radiol. 2020. Vol. 122. doi:10.1016/j.ejrad.2019.108747.; Jeon S.K., Kim J.H., Yoo J. et al. Assessment of malignant potential in intraductal papillary mucinous neoplasms of the pancreas using MR findings and texure analysis // Eur. Radiol. 2021. Vol. 31, No. 5. Р. 3394–3404. doi:10.1007/s00330-020-07425-0.; Ștefan P.A., Lupean R.A., Lebovici A. et al. Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach // Healthcare (Basel). 2022. Vol. 10, No. 6. doi:10.3390/healthcare10061039.; Rosenkrantz A. Histogram-based apparent diffusion coefficient analysis: an emerging tool for cervical cancer characterization // AJR Am. J. Roentgenol. 2013. Vol. 200, No. 2. Р. 311–313. doi:10.2214/AJR.12.9926.; Shen X., Yang F., Yang P. et al. A Contrast-Enhanced Computed Tomography Based Radiomics Approach for Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes: A Feasibility Study // Front Oncol. 2020. Vol. 10. doi:10.3389/fonc.2020.00248.; Dong Z., Chen X., Cheng Z. et al. Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system // Front Oncol. 2022. Vol. 12. doi:10.3389/fonc.2022.941744.; Wei R., Lin K., Yan W. et al. Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images // Technol. Cancer Res. Treat. 2019. Vol. 18. doi:10.1177/1533033818824339.; https://radiag.bmoc-spb.ru/jour/article/view/1005
-
4Academic Journal
المؤلفون: A. D. Smirnova, G. G. Karmazanovsky, E. V. Kondratyev, N. A. Karelskaya, V. N. Galkin, A. Yu. Popov, B. N. Gurmikov, D. V. Kalinin, А. Д. Смирнова, Г. Г Кармазановский, Е. В. Кондратьев, Н. А. Карельская, В. Н. Галкин, А. Ю. Попов, Б. Н. Гурмиков, Д. В. Калинин
المصدر: Research and Practical Medicine Journal; Том 11, № 1 (2024); 54-69 ; Research'n Practical Medicine Journal; Том 11, № 1 (2024); 54-69 ; 2410-1893 ; 10.17709/2410-1893-2024-11-1
مصطلحات موضوعية: компьютерная томография, texture analysis, radiomics, radiogenomics, MRI, CT, текстурный анализ, радиомика, радиогеномика, магнитно-резонансная томография
وصف الملف: application/pdf
Relation: https://www.rpmj.ru/rpmj/article/view/978/612; Гурмиков Б. Н. Молекулярно-генетические аспекты внутрипеченочного холангиоцеллюлярного рака: обзор литературы. Успехи молекулярной онкологии. 2019;6(1):37–43. https://doi.org/10.17650/2313-805x-2019-6-1-37-43; Кармазановский Г. Г. Роль МСКТ и МРТ в диагностике очаговых заболеваний печени. Анналы хирургической гепатологии. 2019;24(4):91–110. https://doi.org/10.16931/1995-5464.2019491-110; Rizvi S, Khan SA, Hallemeier CL, Kelley RK, Gores GJ. Cholangiocarcinoma – evolving concepts and therapeutic strategies. Nat Rev Clin Oncol. 2018 Feb;15(2):95–111. https://doi.org/10.1038/nrclinonc.2017.157; Razumilava N, Gores GJ. Cholangiocarcinoma. Lancet. 2014 Jun 21;383(9935):2168–79. https://doi.org/10.1016/s0140-6736(13)61903-0; Гурмиков Б. Н., Коваленко Ю. А., Вишневский В. А., Чжао А. В. Внутрипеченочный холангиоцеллюлярный рак: диагностика и лечение. Анналы хирургической гепатологии. 2018;23(4):108–117. https://doi.org/10.16931/1995-5464.20184108-117; Chang YT, Chang MC, Huang KW, Tung CC, Hsu C, Wong JM. Clinicopathological and prognostic significances of EGFR, KRAS and BRAF mutations in biliary tract carcinomas in Taiwan. J Gastroenterol Hepatol. 2014 May;29(5):1119–1125. https://doi.org/10.1111/jgh.12505; Abou-Alfa GK, Macarulla T, Javle MM, Kelley RK, Lubner SJ, Adeva J, et al. Ivosidenib in IDH1-mutant, chemotherapy-refractory cholangiocarcinoma (ClarIDHy): a multicentre, randomised, double-blind, placebo-controlled, phase 3 study. Lancet Oncol. 2020 Jun;21(6):796–807. https://doi.org/10.1016/s1470-2045(20)30157-1 Epub 2020 May 13. Erratum in: Lancet Oncol. 2020 Oct;21(10):e462. Erratum in: Lancet Oncol. 2024 Feb;25(2):e61.; Wang Sh, Wu Y, Lui T, Weng Sh, You H, Wei Y, et al. Amplification and overexpression of the MET gene in intrahepatic cholangiocarcinoma correlate with adverse pathological features and worse clinical outcome. Int J Clin Exp Pathol. 2017;10(6):6809–6817.; Zhang J, Wu Z, Zhao J, Liu S, Zhang X, Yuan F, Shi Y, Song B. Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival. Eur Radiol. 2021 Jun;31(6):3661–3672. https://doi.org/10.1007/s00330-020-07524-y; Sadot E, Simpson AL, Do RK, Gonen M, Shia J, Allen PJ, et al. Cholangiocarcinoma: Correlation between Molecular Profiling and Imaging Phenotypes. PLoS One. 2015 Jul 24;10(7):e0132953. https://doi.org/10.1371/journal.pone.0132953; Попов Е. В., Кривоногов Н. Г., Округин С. А., Сазонова С. И. Радиомический анализ изображений в кардиологии: возможности перспективы применения: обзор литературы. Лучевая диагностика и терапия. 2022;13(2):7–15. https://doi.org/10.22328/2079-5343-2022-13-2-7-15; Ma X, Liu L, Fang J, Rao S, Lv L, Zeng M, et al. MRI features predict microvascular invasion in intrahepatic cholangiocarcinoma. Cancer Imaging. 2020 Jun 23;20(1):40. https://doi.org/10.1186/s40644-020-00318-x; Shao C, Chen J, Chen J, Shi J, Huang L, Qiu Y. Histological classification of microvascular invasion to predict prognosis in intrahepatic cholangiocarcinoma. Int J Clin Exp Pathol. 2017 Jul 1;10(7):7674–7681.; Гурмиков Б. Н., Чжао А. В. и др. Холангиоцеллюлярная карцинома. Монография. М.: «ГЭОТАР-Медиа»; 2021; с. 5–20.; Zhang H, Yang T, Wu M, Shen F. Intrahepatic cholangiocarcinoma: Epidemiology, risk factors, diagnosis and surgical management. Cancer Lett. 2016 Sep 1;379(2):198–205. dhttps://doi.org/10.1016/j.canlet.2015.09.008; Chung YE, Kim MJ, Park YN, Choi JY, Pyo JY, Kim YC, et al. Varying appearances of cholangiocarcinoma: radiologic-pathologic correlation. Radiographics. 2009 May-Jun;29(3):683–700. https://doi.org/10.1148/rg.293085729; Fujita N, Asayama Y, Nishie A, Ishigami K, Ushijima Y, Takayama Y, et al. Mass-forming intrahepatic cholangiocarcinoma: Enhancement patterns in the arterial phase of dynamic hepatic CT – Correlation with clinicopathological findings. Eur Radiol. 2017 Feb;27(2):498–506. https://doi.org/10.1007/s00330-016-4386-3; Chalasani N, Baluyut A, Ismail A, Zaman A, Sood G, Ghalib R, et al. Cholangiocarcinoma in patients with primary sclerosing cholangitis: a multicenter case-control study. Hepatology. 2000 Jan;31(1):7–11. https://doi.org/10.1002/hep.510310103; Nanashima A, Sakamoto I, Hayashi T, Tobinaga S, Araki M, Kunizaki M, et alT. Preoperative diagnosis of lymph node metastasis in biliary and pancreatic carcinomas: evaluation of the combination of multi-detector CT and serum CA19-9 level. Dig Dis Sci. 2010 Dec;55(12):3617–3626. https://doi.org/10.1007/s10620-010-1180-y; Noji T, Kondo S, Hirano S, Tanaka E, Suzuki O, Shichinohe T. Computed tomography evaluation of regional lymph node metastases in patients with biliary cancer. Br J Surg. 2008 Jan;95(1):92–96. https://doi.org/10.1002/bjs.5920; Zhan PC, Yang T, Zhang Y, Liu KY, Li Z, Zhang YY, et al. Radiomics using CT images for preoperative prediction of lymph node metastasis in perihilar cholangiocarcinoma: a multi-centric study. Eur Radiol. 2023 Aug 17. https://doi.org/10.1007/s00330-023-10108-1; Spolverato G, Kim Y, Alexandrescu S, Marques HP, Lamelas J, Aldrighetti L, et al. Management and Outcomes of Patients with Recurrent Intrahepatic Cholangiocarcinoma Following Previous Curative-Intent Surgical Resection. Ann Surg Oncol. 2016 Jan;23(1):235–243. https://doi.org/10.1245/s10434-015-4642-9; Ciresa M, De Gaetano AM, Pompili M, Saviano A, Infante A, Montagna M, et al. Enhancement patterns of intrahepatic mass-forming cholangiocarcinoma at multiphasic computed tomography and magnetic resonance imaging and correlation with clinicopathologic features. Eur Rev Med Pharmacol Sci. 2015 Aug;19(15):2786–2797.; Park HJ, Park B, Park SY, Choi SH, Rhee H, Park JH, et al. Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features. Eur Radiol. 2021 Nov;31(11):8638–8648. https://doi.org/10.1007/s00330-021-07926-6; Hu LS, Weiss M, Popescu I, Marques HP, Aldrighetti L, Maithel SK, et al. Impact of microvascular invasion on clinical outcomes after curative-intent resection for intrahepatic cholangiocarcinoma. J Surg Oncol. 2019 Jan;119(1):21–29. https://doi.org/10.1002/jso.25305; Zhang L, Yu X, Wei W, Pan X, Lu L, Xia J, et al. Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: correlation with pathology. Eur Radiol. 2020 Oct;30(10):5327–5336. https://doi.org/10.1007/s00330-020-06895-6; Silva M, Maddalo M, Leoni E, Giuliotti S, Milanese G, Ghetti C, et al. Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics. Abdom Radiol (NY). 2021 Oct;46(10):4689–4700. https://doi.org/10.1007/s00261-021-03183-9; Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020 Apr;21(4):387–401. https://doi.org/10.3348/kjr.2019.0752; Pavic M, Bogowicz M, Würms X, Glatz S, Finazzi T, Riesterer O, et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites. Acta Oncol. 2018 Aug;57(8):1070–1074. https://doi.org/10.1080/0284186x.2018.1445283; Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44(3):1050–1062. https://doi.org/10.1002/mp.12123; Grobmyer SR, Wang L, Gonen M, Fong Y, Klimstra D, D'Angelica M, et al. Perihepatic lymph node assessment in patients undergoing partial hepatectomy for malignancy. Ann Surg. 2006 Aug;244(2):260–264. https://doi.org/10.1097/01.sla.0000217606.59625.9d; Zhang S, Huang S, He W, Wei J, Huo L, Jia N, et al. Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography. Ann Surg Oncol. 2022 Oct;29(11):6786–6799. https://doi.org/10.1245/s10434-022-12028-8; Liang W, Xu L, Yang P, Zhang L, Wan D, Huang Q, et al. Novel Nomogram for Preoperative Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma. Front Oncol. 2018 Sep 4;8:360. https://doi.org/10.3389/fonc.2018.00360; Chu H, Liu Z, Liang W, Zhou Q, Zhang Y, Lei K, et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma. Eur Radiol. 2021 Apr;31(4):2368–2376. https://doi.org/10.1007/s00330-020-07250-5; Zhu Y, Mao Y, Chen J, Qiu Y, Guan Y, Wang Z, He J. Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep. 2021 Sep 15;11(1):18347. https://doi.org/10.1038/s41598-021-97796-1; Zhou Y, Zhou G, Zhang J, Xu C, Wang X, Xu P. Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol. 2021 Sep;31(9):6846–6855. https://doi.org/10.1007/s00330-021-07793-1; Qian X, Lu X, Ma X, Zhang Y, Zhou C, Wang F, et al. A Multi-Parametric Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion Status in Intrahepatic Cholangiocarcinoma. Front Oncol. 2022 Feb 24;12:838701. https://doi.org/10.3389/fonc.2022.838701; Yang Y, Zou X, Zhou W, Yuan G, Hu D, Kuang D, et al. Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy. J Magn Reson Imaging. 2022 Sep;56(3):739–751. https://doi.org/10.1002/jmri.28071; Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019 Jun;70(6):1133–1144. https://doi.org/10.1016/j.jhep.2019.02.023; Hennedige TP, Neo WT, Venkatesh SK. Imaging of malignancies of the biliary tract- an update. Cancer Imaging. 2014 Apr 22;14(1):14. https://doi.org/10.1186/1470-7330-14-14; Xu X, Mao Y, Tang Y, Liu Y, Xue C, Yue Q, et al. Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis. Comput Math Methods Med. 2022 Feb 21;2022:5334095. https://doi.org/10.1155/2022/5334095; Banales JM, Marin JJG, Lamarca A, Rodrigues PM, Khan SA, Roberts LR, et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat Rev Gastroenterol Hepatol. 2020 Sep;17(9):557–588. https://doi.org/10.1038/s41575-020-0310-z; Zhu H, Ji K, Wu W, Zhao S, Zhou J, Zhang C, et al. Describing Treatment Patterns for Elderly Patients with Intrahepatic Cholangiocarcinoma and Predicting Prognosis by a Validated Model: A Population-Based Study. J Cancer. 2021 Mar 30;12(11):3114–3125. https://doi.org/10.7150/jca.53978; Perrin T, Midya A, Yamashita R, Chakraborty J, Saidon T, Jarnagin WR, et al. Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging. Abdom Radiol (NY). 2018 Dec;43(12):3271–3278. https://doi.org/10.1007/s00261-018-1600-6; Banerjee S, Wang DS, Kim HJ, Sirlin CB, Chan MG, Korn RL, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology. 2015 Sep;62(3):792–800. https://doi.org/10.1002/hep.27877; Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol. 2018 May;28(5):2058–2067. https://doi.org/10.1007/s00330-017-5146-8; Hoivik EA, Hodneland E, Dybvik JA, Wagner-Larsen KS, Fasmer KE, Berg HF, et al. A radiogenomics application for prognostic profiling of endometrial cancer. Commun Biol. 2021 Dec 6;4(1):1363. https://doi.org/10.1038/s42003-021-02894-5; Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007 Jun;25(6):675–680. https://doi.org/10.1038/nbt1306; Idris T, Barghash M, Kotrotsou A, Huang HJ, Subbiah V, Kaseb AO, et al. CT-based radiogenomic signature to identify isocitrate dehydrogenase(IDH)1/2 mutations in advanced intrahepatic cholangiocarcinoma. Journal of Clinical Oncology. 2019;37(15). https://doi.org/10.1200/jco.2019.37.15_suppl.4081; https://www.rpmj.ru/rpmj/article/view/978
-
5Academic Journal
المؤلفون: Khromova S.V., Karmazanovsky G.G., Karelskaya N.A., Gruzdev I.S.
المساهمون: 1
المصدر: Almanac of Clinical Medicine; Vol 52, No 1 (2024); 25-34 ; Альманах клинической медицины; Vol 52, No 1 (2024); 25-34 ; 2587-9294 ; 2072-0505
مصطلحات موضوعية: renal cell carcinoma, texture analysis, computed tomography, segmentation, reproducibility of results, почечно-клеточный рак, текстурный анализ, компьютерная томография, сегментация, воспроизводимость результатов
وصف الملف: application/pdf
Relation: https://almclinmed.ru/jour/article/view/17231/1645; https://almclinmed.ru/jour/article/view/17231/1648; https://almclinmed.ru/jour/article/view/17231/1652; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159840; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159915; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159916; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159917; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159918; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159919; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159920; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159921; https://almclinmed.ru/jour/article/downloadSuppFile/17231/159922; https://almclinmed.ru/jour/article/view/17231
-
6Academic Journal
المؤلفون: Наталья Николаевна Минакова
المصدر: Ползуновский вестник, Iss 1, Pp 230-236 (2023)
مصطلحات موضوعية: изображение макроструктуры, полимерные композиционные материалы, наполненные техни-ческим углеродом каучуки, модифицированный технический углерод, текстурный анализ, объ-емное электрическое сопротивление, метод локальных линейных шаблонов, гистограммы яр-кости, расстояние бхаттачария, расстояние кульбака–лейблера., Technology
وصف الملف: electronic resource
-
7Academic Journal
المؤلفون: N. V. Petrova, G. G. Karmazanovsky, E. V. Kondratyev, A. Yu. Popov, M. V. Rostovtsev, N. Yu. Germanovich, D. V. Kalinin, Н. В. Петрова, Г. Г. Кармазановский, Е. В. Кондратьев, А. Ю. Попов, М. В. Ростовцев, Н. Ю. Германович, Д. В. Калинин
المصدر: Research and Practical Medicine Journal; Том 10, № 3 (2023); 69-79 ; Research'n Practical Medicine Journal; Том 10, № 3 (2023); 69-79 ; 2410-1893 ; 10.17709/10.17709/2410-1893-2023-10-3
مصطلحات موضوعية: текстурный анализ, neoadjuvant therapy, MRI, pathological complete response, radiomics, texture analysis, неоадъювантная терапия, МРТ, патологический полный ответ, радиологический полный ответ, радиогеномика
وصف الملف: application/pdf
Relation: https://www.rpmj.ru/rpmj/article/view/901/584; Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021 May;71(3):209–249. https://doi.org/10.3322/caac.21660; Tan W, Yang M, Yang H, Zhou F, Shen W. Predicting the response to neoadjuvant therapy for early-stage breast cancer: Tumor-, blood-, and imaging-related biomarkers. Cancer Manag Res. 2018 Oct 9;10:4333–4347. https://doi.org/10.2147/cmar.s174435; Puglisi F, Follador A, Minisini AM, Cardellino GG, Russo S, Andreetta C, Di Terlizzi S, Piga A. Baseline staging tests aſter a new diagnosis of breast cancer: further evidence of their limited indications. Ann Oncol. 2005 Feb;16(2):263–266. https://doi.org/10.1093/annonc/mdi063; Liedtke C, Mazouni C, Hess KR, André F, Tordai A, Mejia JA, Symmans WF, Gonzalez-Angulo AM, Hennessy B, Green M, Cristofanilli M, Hortobagyi GN, Pusztai L. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008 Mar 10;26(8):1275–1281. https://doi.org/10.1200/jco.2007.14.4147. Epub 2008 Feb 4. Corrected and republished in: J Clin Oncol. 2023 Apr 1;41(10):1809-1815.; Funt SA, Chapman PB. The Role of Neoadjuvant Trials in Drug Development for Solid Tumors. Clin Cancer Res. 2016 May 15;22(10):2323–2328. https://doi.org/10.1158/1078-0432.ccr-15-1961; Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. Lancet Oncol. 2018 Jan;19(1):27–39. https://doi.org/10.1016/s1470-2045(17)30777-5; Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet. 2014 Jul 12;384(9938):164-172. https://doi.org/10.1016/s0140-6736(13)62422-8. Erratum in: Lancet. 2019 Mar 9;393(10175):986.; Gradishar WJ, Moran MS, Abraham J, Aſt R, Agnese D, Allison KH, et al. Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2023 Mar.;23 (Version 4.2023). Available at: https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf; Korde LA, Somerfield MR, Carey LA, Crews JR, Denduluri N, Hwang ES, et al. Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO Guideline. J Clin Oncol. 2021 May 1;39(13):1485–1505. https://doi.org/10.1200/jco.20.03399; Schneeweiss A, Chia S, Hickish T, Harvey V, Eniu A, Hegg R, et al. Pertuzumab plus trastuzumab in combination with standard neoadjuvant anthracycline-containing and anthracycline-free chemotherapy regimens in patients with HER2-positive early breast cancer: A randomized phase II cardiac safety study (TRYPHAENA). Ann Oncol. 2013 Sep;24(9):2278–2284. https://doi.org/10.1093/annonc/mdt182; Sikov WM, Berry DA, Perou CM, Singh B, Cirrincione CT, Tolaney SM, et al. Impact of the addition of carboplatin and/or bevacizumab to neoadjuvant once-per-week paclitaxel followed by dose-dense doxorubicin and cyclophosphamide on pathologic complete response rates in stage II to III triple-negative breast cancer: CALGB 40603 (Alliance). J Clin Oncol. 2015 Jan 1;33(1):13–21. https://doi.org/10.1200/jco.2014.57.0572; Garutti M, Griguolo G, Botticelli A, Buzzatti G, De Angelis C, Gerratana L, et al. Definition of High-Risk Early Hormone-Positive HER2-Negative Breast Cancer: A Consensus Review. Cancers (Basel). 2022 Apr 9;14(8):1898. https://doi.org/10.3390/cancers14081898; Kong X, Moran MS, Zhang N, Haffty B, Yang Q. Meta-Analysis Confirms Achieving Pathological Complete Response Aſter Neoadjuvant Chemotherapy Predicts Favourable Prognosis for Breast Cancer Patients. Eur J Cancer. 2011;47(14):2084–2090. https://doi.org/10.1016/j.ejca.2011.06.014; Scheel JR, Kim E, Partridge SC, Lehman CD, Rosen MA, Bernreuter WK, et al. MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response Aſter Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial. AJR Am J Roentgenol. 2018;210(6):1376–1385. https://doi.org/10.2214/ajr.17.18323; Gampenrieder SP, Peer A, Weismann C, Meissnitzer M, Rinnerthaler G, Webhofer J, et al. Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) aſter neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR). Breast Cancer Res. 2019 Jan 31;21(1):19. https://doi.org/10.1186/s13058-018-1091-y; O’Donnell JPM, Gasior SA, Davey MG, O’Malley E, Lowery AJ, McGarry J, et al. The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis. Eur J Radiol. 2022 Dec;157:110561. https://doi.org/10.1016/j.ejrad.2022.110561; Hong J, Rui W, Fei X, Chen X, Shen K. Association of tumor-infiltrating lymphocytes before and aſter neoadjuvant chemotherapy with pathological complete response and prognosis in patients with breast cancer. Cancer Med. 2021 Nov;10(22):7921–7933. https://doi.org/10.1002/cam4.4302; Teng MW, Ngiow SF, Ribas A, Smyth MJ. Classifying Cancers Based on T-cell Infiltration and PD-L1. Cancer Res. 2015 Jun 1;75(11):2139–2145. https://doi.org/10.1158/0008-5472.can-15-0255; Yamaguchi R, Tanaka M, Yano A, Tse GM, Yamaguchi M, Koura K, et al. Tumor-infiltrating lymphocytes are important pathologic predictors for neoadjuvant chemotherapy in patients with breast cancer. Hum Pathol. 2012 Oct;43(10):1688–1694. https://doi.org/10.1016/j.humpath.2011.12.013; Verma C, Kaewkangsadan V, Eremin JM, Cowley GP, Ilyas M, El-Sheemy MA, Eremin O. Natural killer (NK) cell profiles in blood and tumour in women with large and locally advanced breast cancer (LLABC) and their contribution to a pathological complete response (PCR) in the tumour following neoadjuvant chemotherapy (NAC): differential restoration of blood profiles by NAC and surgery. J Transl Med. 2015 Jun 4;13:180. https://doi.org/10.1186/s12967-015-0535-8; Malyguine AM, Strobl SL, Shurin MR. Immunological monitoring of the tumor immunoenvironment for clinical trials. Cancer Immunol Immunother. 2012 Feb;61(2):239–247. https://doi.org/10.1007/s00262-011-1148-6; Li C, Lu N, He Z, Tan Y, Liu Y, Chen Y, et al. A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Ann Surg Oncol. 2022 Nov;29(12):7685–7693. https://doi.org/10.1245/s10434-022-12034-w; Gu YL, Pan SM, Ren J, Yang ZX, Jiang GQ. Role of Magnetic Resonance Imaging in Detection of Pathologic Complete Remission in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy: A Meta-analysis. Clin Breast Cancer. 2017 Jul;17(4):245–255. https://doi.org/10.1016/j.clbc.2016.12.010; Cao K, Zhao B, Li XT, Li YL, Sun YS. Texture Analysis of Dynamic Contrast-Enhanced MRI in Evaluating Pathologic Complete Response (pCR) of Mass-Like Breast Cancer aſter Neoadjuvant Therapy. J Oncol. 2019 Dec 26;2019:4731532. https://doi.org/10.1155/2019/4731532; Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2019 Jun;49(7):e101–e121. https://doi.org/10.1002/jmri.26518; https://www.rpmj.ru/rpmj/article/view/901
-
8Academic Journal
المؤلفون: E. N. Surovcev, A. V. Kapishnikov, A. V. Kolsanov, Е. Н. Суровцев, А. В. Капишников, А. В. Колсанов
المصدر: Research and Practical Medicine Journal; Том 10, № 2 (2023); 50-61 ; Research'n Practical Medicine Journal; Том 10, № 2 (2023); 50-61 ; 2410-1893 ; 10.17709/2410-1893-2023-10-2
مصطلحات موضوعية: текстурный анализ, magnetic resonance imaging, meningiomas, radiomics, texture analysis, магнитно-резонансная томография, менингиомы, радиомика
وصف الملف: application/pdf
Relation: https://www.rpmj.ru/rpmj/article/view/886/551; https://www.rpmj.ru/rpmj/article/view/886/562; Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP, Stegmayr C, et al. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods. 2021 Apr;188:112–121. https://doi.org/10.1016/j.ymeth.2020.06.003; Ostrom QT, Cioffi G, Gittleman H, Patil N, Waite K, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016. Neuro Oncol. 2019 Nov 1;21(Suppl 5):v1–v100. https://doi.org/10.1093/neuonc/noz150; Осборн А.Г., Зальцман К.Л., Завери М.Д. Лучевая диагностика. Головной мозг. Пер. с англ. Д.И. Волобуева. 3-е изд. М.: Из-во Панфилова; 2018, 1216 с.; Saigal G, Pisani L, Allakhverdieva E, Aristizabal J, Lehmkuhl D, Contreras F, et al. Utility of Microhemorrhage as a Diagnostic Tool in Distinguishing Vestibular Schwannomas from other Cerebellopontine Angle (CPA) Tumors. Indian J Otolaryngol Head Neck Surg. 2021 Sep;73(3):321–326. https://doi.org/10.1007/s12070-021-02372-8; Fountain DM, Young AMH, Santarius T. Malignant meningiomas. Handb Clin Neurol. 2020;170:245–250. https://doi.org/10.1016/b978-0-12-822198-3.00044-6; Kabashi S, Ugurel MS, Dedushi K, Mucaj S. The Role of Magnetic Resonance Imaging (MRI) in Diagnostics of Acoustic Schwannoma. Acta Inform Med. 2020 Dec;28(4):287–291. 10.5455/aim.2020.28.287-291. https://doi.org/10.5455/aim.2020.28.287-291; Yan PF, Yan L, Zhang Z, Salim A, Wang L, Hu TT, Zhao HY. Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases. Int J Surg. 2016 Dec;36(Pt A):109–117. https://doi.org/10.1016/j.ijsu.2016.10.023; Ugga L, Spadarella G, Pinto L, Cuocolo R, Brunetti A. Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization. Cancers (Basel). 2022 May 25;14(11):2605. https://doi.org/10.3390/cancers14112605; Компьютерная программа для расчета гистограммных и текстурных параметров изображений MaZda ver.4.6. Режим доступа: http://www.eletel.p.lodz.pl/programy/mazda/index.php?action=mazda_46; Капишников А. В., Суровцев Е. Н., Удалов Ю. Д. Магнитно-резонансная томография первичных внемозговых опухолей: проблемы диагностики и перспективы радиомики. Медицинская радиология и радиационная безопасность. 2022;67(4):49–56. https://doi.org/10.33266/1024-6177-2022-67-4-49-56; Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol. 2021 Nov 9;20:69–75. https://doi.org/10.1016/j.phro.2021.10.007; Coroller TP, Bi WL, Huynh E, Abedalthagafi M, Aizer AA, Greenwald NF, et al. Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS One. 2017 Nov 16;12(11):e0187908. https://doi.org/10.1371/journal.pone.0187908; Agafonova YD, Gaidel AV, Surovtsev EN, Kapishnikov AV. Meningioma Detection in MR Images Using Convolutional Neural Network and Computer Vision Methods. Journal of Biomedical Photonics & Engineering [Internet]. 2020 Sep 30;030301. https://doi.org/10.18287/jbpe20.06.030301; Халафян А.А. Statistica 6: статистический анализ данных. Учебное пособие. М.: Бином-Пресс; 2008, 512 с.; Fatima N, Maxwell AK, La Dine A, Barnard ZR, Mehta GU, Wilkinson EP, et al. Predictors of hearing functional outcome following surgery for cerebellopontine angle meningioma. J Neurooncol. 2022 Mar;157(1):165–176. https://doi.org/10.1007/s11060-022-03958-0; https://www.rpmj.ru/rpmj/article/view/886
-
9Academic Journal
المؤلفون: A. E. Solopova, J. V. Nosova, B. B. Bendzhenova, А. Е. Солопова, Ю. В. Носова, Б. Б. Бендженова
المصدر: Obstetrics, Gynecology and Reproduction; Vol 17, No 4 (2023); 500-511 ; Акушерство, Гинекология и Репродукция; Vol 17, No 4 (2023); 500-511 ; 2500-3194 ; 2313-7347
مصطلحات موضوعية: онкология, СС, radiomics, texture analysis, oncology, РШМ, радиомика, текстурный анализ
وصف الملف: application/pdf
Relation: https://www.gynecology.su/jour/article/view/1768/1137; Bray F., Ferlay J., Soerjomataram I. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. https://doi.org/10.3322/caac.21492.; Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol Obstet. 2009;105(2):103–4. https://doi.org/10.1016/j.ijgo.2009.02.012.; Petsuksiri1 J., Jaishuen A., Pattaranutaporn P., Chansilpa Y. Advanced imaging applications for locally advanced cervical cancer. Asian Pacific J Cancer Prev. 2012;13(5):1713–8. https://doi.org/10.7314/APJCP.2012.13.5.1713.; Bhatla N., Aoki D., Sharma D.N., Sankaranarayanan R. Cancer of the cervix uteri. Int J Gynecol Obstet. 2018;143 Suppl 2:22–36. https://doi.org/10.1002/ijgo.12611.; Sala E., Rockall A.G., Freeman S.J. et al. The added role of MR imaging in treatment stratification of patients with gynecologic malignancies: what the radiologist needs to know. Radiology. 2013;266(3);717–40. https://doi.org/10.1148/radiol.12120315.; Gui B., Miccò M., Valentini A.L. et al. Prospective multimodal imaging assessment of locally advanced cervical cancer patients administered by chemoradiation followed by radical surgery – the "PRICE" study 2: role of conventional and DW-MRI. Eur Radiol. 2019;29(4):2045–57. https://doi.org/10.1007/s00330-018-5768-5.; Valentini A.L., Miccò M., Gui B. et al. The PRICE study: The role of conventional and diffusion-weighted magnetic resonance imaging in assessment of locally advanced cervical cancer patients administered by chemoradiation followed by radical surgery. Eur Radiol. 2018;28(6):2425–35. https://doi.org/10.1007/s00330-017-5233-x.; Jalaguier-Coudray A., Villard-Mahjoub R., Delouche A. et al. Value of dynamic contrast-enhanced and diffusion-weighted MR imaging in the detection of pathologic complete response in cervical cancer after neoadjuvant therapy: A retrospective observational study. Radiology. 2017;284(2):432–42. https://doi.org/10.1148/radiol.2017161299.; Schreuder S.M., Lensing R., Stoker J., Bipat S. Monitoring treatment response in patients undergoing chemoradiotherapy for locally advanced uterine cervical cancer by additional diffusion-weighted imaging: A systematic review. J Magn Reson Imaging. 2015;42(3):572–94. https://doi.org/10.1002/jmri.24784.; Lambin P., Rios-Velazquez E., Leijenaar R. et al. Radiomics: extracting more Information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6. https://doi.org/10.1016/j.ejca.2011.11.036.; Lucia F., Visvikis D., Desseroit M.-C. et al. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2018;45(5):768–86. https://doi.org/10.1007/s00259-017-3898-7.; Torheim T., Groendahl A.R., Andersen E.K.F. et al. Cluster analysis of dynamic contrast enhanced MRI reveals tumor subregions related to locoregional relapse for cervical cancer patients. Acta Oncol. 2016;55(11):1294–8. https://doi.org/10.1080/0284186X.2016.1189091.; Koh W.-J., Abu-Rustum N.R., Bean S. et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2019;17(1):64–84. https://doi.org/10.6004/jnccn.2019.0001.; Quinn M.A., Benedet J.L., Odicino F. et al. Carcinoma of the cervix uteri. FIGO 26th annual report on the results of treatment in gynecological cancer. Int J Gynaecol Obstet. 2006;95 Suppl 1:S43–103. https://doi.org/10.1016/S0020-7292(06)60030-1.; Guan Y., Li W., Jiang Z. et al. Whole-lesion apparent Diffusion coefficient-based entropy-related parameters for characterizing cervical cancers: initial findings. Acad Radiol. 2016;23(12):1559–67. https://doi.org/10.1016/j.acra.2016.08.010.; Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer Meta-analysis Collaboration. Neoadjuvant chemotherapy for locally advanced cervical cancer: a systematic review and meta-analysis of individual patient data from 21 randomised trials. Eur J Cancer.2003;39(17):2470–86. https://doi.org/10.1016/s0959-8049(03)00425-8.; Michel G., Morice P., Castaigne D. et al. Lymphatic spread in stage Ib and II cervical carcinoma: anatomy and surgical implications. Obstet Gynecol. 1998;91(3):360–3. https://doi.org/10.1016/s0029-7844(97)00696-0.; Bhatla N., Berek J.S., Fredes M.C. et al. Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstet. 2019;145(1):129–35. https://doi.org/10.1002/ijgo.12749.; Manganaro L., Lakhman Y., Bharwani N. et al. Staging, recurrence and follow-up of uterine cervical cancer using MRI: updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018. Eur Radiol. 2021;31(10):7802–16. https://doi.org/10.1007/s00330-020-07632-9.; Rockall A.G., Ghosh S., Alexander-Sefre F. еt al. Can MRI rule out bladder and rectal invasion in cervical cancer to help select patients for limited EUA. Gynecol Oncol. 2006;101(2):244–9. https://doi.org/10.1016/j.ygyno.2005.10.012.; Кwee T.C., Takahara T., Ochiai R. et al. Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): features and potentional applications in oncology. Eur Radiol. 2008;18(9):1937–52. https://doi.org/10.1007/s00330-008-0968-z.; Koh D.M, Collins D.J. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol. 2007;188(6):1622–35. https://doi.org/10.2214/AJR.06.1403.; Figueiras R.G., Goh V., Padhani A.R. et al. The role of functional imaging in colorectal cancer. AJR Am J Roentgenol. 2010;195(1):54–66. https://doi.org/10.2214/AJR.10.4422.; Kuang F., Ren J., Zhong Q. et al. The value of apparent diffusion coefficient in the assessment of cervical cancer. Eur Radiol. 2013;23(4):1050–8. https://doi.org/10.1007/s00330-012-2681-1.; Liu Y., Bai R., Sun H. et al. Diffusion weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol. 2009;64(11):1067–74. https://doi.org/10.1016/j.crad.2009.07.010.; Chen Y.B., Hu C.M., Chen G.L. et al. Staging of uterine cervical carcinoma: whole body diffusion-weighted magnetic resonance imaging. Abdom Imaging. 2011;36(5):619–26. https://doi.org/10.1007/s00261-010-9642-4.; Qi Y.-F., He Y.-L., Lin C.-Y. et al. Diffusion-weighted imaging of cervical cancer: feasibility of ultra-high b-value at 3T. Eur J Radiol. 2020;124:108779. https://doi.org/10.1016/j.ejrad.2019.108779.; Chung H.H., Kang S.-B., Cho J.Y. et al. Can preoperative MRI accurately evaluate nodal and parametrial invasion in early stagе cervical cancer? Jpn J Clin Oncol. 2007;37(5):370–5. https://doi.org/10.1093/jjco/hym036.; Kim S.H., Han M.C. Invasion of the urinary bladder by uterine cervical carcinoma: evaluation with MR imaging. AJR Am J Roentgenol. 1997;168(2):393–7. https://doi.org/10.2214/ajr.168.2.9016214.; Mirestean C.C., Pagute O., Buzea C. et al. Radiomic machine learning and texture analysis – new horizons for head and neck oncology. Maedica (Bucur). 2019;14(2):126–30. https://doi.org/10.26574/maedica.2019.14.2.126.; Giganti F., Antunes S., Salerno A. et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. 2017;27(5):1831–9. https://doi.org/10.1007/s00330-016-4540-y.; Beig N., Khorrami M., Alilou M. et al. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology. 2019;290(3):783–92. https://doi.org/10.1148/radiol.2018180910.; Sidhu H.S., Benigno S., Ganeshan B. et al. Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur Radiol. 2017;27(6):2348–58. https://doi.org/10.1007/s00330-016-4579-9.; Ueno Y., Forghani B., Forghani R. et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification – a preliminary analysis. Radiology. 2017;284(3):748–57. https://doi.org/10.1148/radiol.2017161950.; Lakhman Y., Veeraraghavan H., Chaim J. et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol. 2017;27(7):2903–15. https://doi.org/10.1007/s00330-016-4623-9.; De Cecco C.N., Ciolina M., Caruso D. et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY). 2016;41(9):1728–35. https://doi.org/10.1007/s00261-016-0733-8.; Liu Z., Zhang X.Y., Shi Y.J. et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23(23):7253–62. https://doi.org/10.1158/1078-0432.CCR-17-1038.; Gardin I., Grégoire V., Gibon D. et al. Radiomics: principles and radiotherapy applications. Crit Rev Oncol Hematol. 2019;138:44–50. https://doi.org/10.1016/j.critrevonc.2019.03.015.; Chaddad A., Kucharczyk M.J., Daniel P. et al. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol. 2019;9:374. https://doi.org/10.3389/fonc.2019.00374.; van Velden F.H., Kramer G.M., Frings V. et al. Repeatability of radiomic features in non-small-cell lung cancer [18F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016;18(5):788–95. https://doi.org/10.1007/s11307-016-0940-2.; Nioche C., Orlhac F., Boughdad S. et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9. https://doi.org/10.1158/0008-5472.can-18-0125.; Zhang L., Fried D.V., Fave X.J. et al. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42(3):1341–53. https://doi.org/10.1118/1.4908210.; Wu J., Tha K.K., Xing L., Li R. Radiomics and radiogenomics for precision radiotherapy. J Radiat Res. 2018;59(suppl_1):i25–i31. https://doi.org/10.1093/jrr/rrx102.; Coroller T.P., Grossmann P., Hou Y. et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345–50. https://doi.org/10.1016/j.radonc.2015.02.015.; Mattonen S.A., Palma D.A., Johnson C. et al. Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment. Int J Radiat Oncol Biol Phys. 2016;94(5):1121–8. https://doi.org/10.1016/j.ijrobp.2015.12.369.; Cook G.J., Yip C., Siddique M. et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54(1):19–26. https://doi.org/10.2967/jnumed.112.107375.; Huang Y., Liu Z., He L. et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology. 2016;281(3):947–57. https://doi.org/10.1148/radiol.2016152234.; Stanzione A., Cuocolo R., Del Grosso R. et al. Deep myometrial infiltration of endometrial cancer on MRI: a radiomics-powered machine learning pilot study. Acad Radiol. 2021;28(5):737–44. https://doi.org/10.1016/j.acra.2020.02.028.; Miccò M., Gui B., Russo L. et al. Preoperative tumor texture analysis on MRI for high-risk disease prediction in endometrial cancer: a hypothesis-generating study. J Pers Med. 2022;12(11):1854. https://doi.org/10.3390/jpm12111854.; Cheng M., Tan S., Ren T. et al. Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers. Front Oncol. 2023;12:1073983. https://doi.org/10.3389/fonc.2022.1073983.; Fang M., Kan Y., Dong D. et al. Multi-habitat based radiomics for the prediction of treatment response to concurrent chemotherapy and radiation therapy in locally advanced cervical cancer. Front Oncol. 2020;10:563. https://doi.org/10.3389/fonc.2020.00563.; Gien L., Gien L.T., Covens A. Lymph node assessment in cervical cancer: prognostic and therapeutic implications. J Surg Oncol. 2009;99(4):242–47. https://doi.org/10.1002/jso.21199.; Small W., Bacon M.A., Bajaj A. et al. Cervical cancer: a global health crisis.Cancer. 2017;123(13):2404–12. https://doi.org/10.1002/cncr.30667.; Ferrandina G., Anchora L.P., Gallotta V. et al. Can we define the risk of lymph node metastasis in early-stage cervical cancer patients? A largescale, retrospective study. Ann Surg Oncol. 2017;24(8):2311–8. https://doi.org/10.1245/s10434-017-5917-0.; Macdonald M.C., Tidy J.A. Can we be less radical with surgery for early cervical cancer? Curr Oncol Rep. 2016;18(3):16. https://doi.org/10.1007/s11912-016-0501-5.; Kadkhodayan S., Hasanzadeh M., Treglia G. et al. Sentinel node biopsy for lymph nodal staging of uterine cervix cancer: a systematic review and meta-analysis of the pertinent literature. Eur J Surg Oncol. 2015;41(1):1–20. https://doi.org/10.1016/j.ejso.2014.09.010.; Wang T., Gao T., Yang J. et al. Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur J Radiol. 2019;114:128–35. https://doi.org/10.1016/j.ejrad.2019.01.003.; Becker A.S., Ghafoor S., Marcon M. et al. MRI texture features may predict differentiation and nodal stage of cervical cancer: a pilot study. Acta Radiol Open. 2017;6(10):205846011772957. https://doi.org/10.1177/2058460117729574.; Li X.X., Lin T.-T., Liu B., Wei W. Diagnosis of cervical cancer with parametrial invasion on whole-tumor dynamic contrast-enhanced magnetic resonance imaging combined with whole-lesion texture analysis based on T2-weighted images. Front Bioeng Biotechnol. 2020;8:590. https://doi.org/10.3389/fbioe.2020.00590.; Avanzo M., Stancanello J., El Naqa I. Beyond imaging: the promise of radiomics. Phys Med. 2017;38:122–39. https://doi.org/10.1016/j.ejmp.2017.05.071.; https://www.gynecology.su/jour/article/view/1768
-
10Academic Journal
المؤلفون: E. M. Syrkashev, A. A. Burov, Yu. L. Podurovskaya, F. Z. Kadyrberdiyeva, E. O. Ikryannikov, D. S. Semenov, D. E. Sharova, Yu. A. Vasilev, A. I. Gus, Е. М. Сыркашев, А. А. Буров, Ю. Л. Подуровская, Ф. З. Кадырбердиева, Е. О. Икрянников, Д. С. Семенов, Д. Е. Шарова, Ю. А. Васильев, А. И. Гус
المصدر: Medical Visualization; Том 28, № 1 (2024); 157-167 ; Медицинская визуализация; Том 28, № 1 (2024); 157-167 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: врожденная диафрагмальная грыжа, radiomics, obstetrics and gynecology, texture analysis, congenital diaphragmatic hernia, радиомика, акушерство и гинекология, текстурный анализ
وصف الملف: application/pdf
Relation: https://medvis.vidar.ru/jour/article/view/1359/858; Lambin P., Rios-Velazquez E., Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012; 48 (4): 441–446. http://doi.org/10.1016/j.ejca.2011.11.036; Kumar V., Gu Y., Basu S. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012; 30 (9): 1234–1248. http://doi.org/10.1016/j.mri.2012.06.010; Mirestean C.C., Pagute O., Buzea C. et al. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019; 14 (2): 126–130. http://doi.org/10.26574/maedica.2019.14.2.126; Говорухина В.Г., Семенов С.С., Гележе П.Б., Диденко В.В., Морозов С.П., Андрейченко А.Е. Роль маммографии в радиомике рака молочной железы. Digital Diagnostics. 2021; 2 (2): 185–199. http://doi.org/10.17816/DD70479; Bae S., Choi Y.S., Ahn S.S. et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology. 2018; 289 (3): 797–806. http://doi.org/10.1148/radiol.2018180200; Coroller T.P., Agrawal V., Huynh E. et al. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J. Thorac. Oncol. 2017; 12 (3): 467–476. http://doi.org/10.1016/j.jtho.2016.11.2226; Du Y., Fang Z., Jiao J. et al. Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and/or pre-eclampsia. Ultrasound Obstet Gynecol. 2021; 57 (5): 804–812. http://doi.org/10.1002/uog.22037; Watzenboeck M.L., Heidinger B.H., Rainer J. et al. Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study. Insights. Imaging. 2023; 14 (1): 31. http://doi.org/10.1186/s13244-023-01376-y; Heinzerling N., Wagner A. Fetal Diagnosis and Therapy: A Reference Handbook for Pediatric Surgeons. Fetal Diagn. Ther. 2013.; Jokhi R.P., Whitby E.H. Magnetic resonance imaging of the fetus. Dev. Med. Child Neurol. 53 (1): 18–28. http://doi.org/10.1111/j.1469-8749.2010.03813.x; Prayer D., Malinger G., De Catte L. et al.; ISUOG Clinical Standards Committee. ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging. Ultrasound Obstet Gynecol. 2023; 61 (2): 278–287. http://doi.org/10.1002/uog.26129; Prayer D., Malinger G., Brugger P.C. et al. ISUOG Practice Guidelines: performance of fetal magnetic resonance imaging. Ultrasound Obstet. Gynecol. 2017; 49 (5): 671–680. http://doi.org/10.1002/uog.17412; Dütemeyer V., Cordier A.G., Cannie M.M. et al. Prenatal prediction of postnatal survival in fetuses with congenital diaphragmatic hernia using MRI: lung volume measurement, signal intensity ratio, and effect of experience. J. Matern. Fetal Neonatal Med. 2022; 35 (6): 1036–1044. http://doi.org/10.1080/14767058.2020.1740982; Сыркашев Е.М., Солопова А.Е., Быченко В.Г., Буров А.А., Подуровская Ю.Л., Гус А.И. Антенатальная биометрия легких при врожденной диафрагмальной грыже по данным МРТ. REJR. 2020; 10 (4): 169–178. http://doi.org/10.21569/2222-7415-2020-10-4-169-178; Farrugia M.K., Raza S.A., Gould S., Lakhoo K. Congenital lung lesions: classification and concordance of radiological appearance and surgical pathology. Pediatr. Surg. Int. 2008; 24 (9): 987–991. http://doi.org/10.1007/s00383-008-2201-1; Zani A., Chung W.K., Deprest J. et al. Congenital diaphragmatic hernia. Nat. Rev. Dis. Primers. 2022; 8 (1): 37. http://doi.org/10.1038/s41572-022-00362-w; Yushkevich P.A., Gao Y., Gerig G. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA. 2016; 3342–3345. http://doi.org/10.1109/EMBC.2016.7591443; Shchegolev A.I., Tumanova U.N. Pulmonary hypoplasia: causes and pathological finding. Int. J. Appl. Fundam. Res. 2017; 4: 101–153.; Ogawa R., Kido T., Nakamura M. et al. Magnetic resonance assessment of fetal lung maturity: comparison between signal intensity and volume measurement. Jpn. J. Radiol. 2018; 36 (7): 444–449. http://doi.org/10.1007/s11604-018-0745-0; Keller T.M., Rake A., Michel S.C. et al. MR assessment of fetal lung development using lung volumes and signal intensities. Eur. Radiol. 2004; 14 (6): 984–989. http://doi.org/10.1007/s00330-004-2256-x; Oka Y., Rahman M., Sasakura C. et al. Prenatal diagnosis of fetal respiratory function: evaluation of fetal lung maturity using lung-to-liver signal intensity ratio at magnetic resonance imaging. Prenat. Diagn. 2014; 34 (13): 1289–1294. http://doi.org/10.1002/pd.4469; Moshiri M., Mannelli L., Richardson M.L. et al. Fetal lung maturity assessment with MRI fetal lung-to-liver signal-intensity ratio. Am. J. Roentgenol. 2013; 201 (6): 1386–1390. http://doi.org/10.2214/AJR.12.9679; Mills M., Winter T.C., Kennedy A.M., Woodward P.J. Determination of fetal lung maturity using magnetic resonance imaging signal intensity measurements. Ultrasound Q. 2014; 30 (1): 61–67. http://doi.org/10.1097/RUQ.0000000000000054; Yamoto M., Iwazaki T., Takeuchi K. et al. The fetal lung-to-liver signal intensity ratio on magnetic resonance imaging as a predictor of outcomes from isolated congenital diaphragmatic hernia. Pediatr. Surg. Int. 2018; 34 (2): 161–168. http://doi.org/10.1007/s00383-017-4184-2; Cordier A.G., Russo F.M., Deprest J., Benachi A. Prenatal diagnosis, imaging, and prognosis in Congenital Diaphragmatic Hernia. Semin. Perinatol. 2020; 44 (1): 51163. http://doi.org/10.1053/j.semperi.2019.07.002; Madenci A.L., Church J.T., Gajarski R.J. et al. Pulmonary Hypertension in Patients with Congenital Diaphragmatic Hernia: Does Lung Size Matter? Eur. J. Pediatr. Surg. 2018; 28 (6): 508–514. http://doi.org/10.1055/s-0037-1607291; Basurto D., Russo F.M., Papastefanou I. et al. Pulmonary hypertension in congenital diaphragmatic hernia: Antenatal prediction and impact on neonatal mortality. Prenat. Diagn. 2022; 42 (10): 1303–1311. http://doi.org/10.1002/pd.6207; Petroze R.T., Caminsky N.G., Trebichavsky J. et al. Prenatal prediction of survival in congenital diaphragmatic hernia: An audit of postnatal outcomes. J. Pediatr. Surg. 2019; 54 (5): 925–931. http://doi.org/10.1016/j.jpedsurg.2019.01.021; Moore R.J., Strachan B., Tyler D.J. et al. In vivo diffusion measurements as an indication of fetal lung maturation using echo planar imaging at 0.5T. Magn. Reson. Med. 2001; 45 (2): 247–253. http://doi.org/10.1002/1522-2594(200102)45:23.0.co;2-m; Balassy C., Kasprian G., Brugger P.C. et al. Diffusion-weighted MR imaging of the normal fetal lung. Eur. Radiol. 2008; 18 (4): 700–706. http://doi.org/10.1007/s00330-007-0784-x; Cannie M., Jani J., De Keyzer F. et al. Diffusion-weighted MRI in lungs of normal fetuses and those with congenital diaphragmatic hernia. Ultrasound Obstet. Gynecol. 2009; 34 (6): 678–686. http://doi.org/10.1002/uog.7326; Manganaro L., Perrone A., Sassi S. et al. Diffusion-weighted MR imaging and apparent diffusion coefficient of the normal fetal lung: preliminary experience. Prenat. Diagn. 2008 Aug;28(8):745–748. http://doi.org/10.1002/pd.2041; Afacan O., Gholipour A., Mulkern R.V. et al. Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age. J. Magn. Reson. Imaging. 2016; 44 (6): 1650–1655. http://doi.org/10.1002/jmri.25294; Ercolani G., Capuani S., Antonelli A. et al. IntraVoxel Incoherent Motion (IVIM) MRI of fetal lung and kidney: Can the perfusion fraction be a marker of normal pulmonary and renal maturation? Eur. J. Radiol. 2021; 139: 109726. http://doi.org/10.1016/j.ejrad.2021.109726; Sethi S., Giza S.A., Goldberg E. et al. Quantification of 1.5 T T1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies. J. Magn. Reson. Imaging. 2021; 54 (1): 113–121. http://doi.org/10.1002/jmri.27547; Prayer F., Watzenböck M.L., Heidinger B.H. et al. Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity. Eur. Radiol. 2023; 33 (6): 4205–4213. http://doi.org/10.1007/s00330-022-09367-1; https://medvis.vidar.ru/jour/article/view/1359
-
11Academic Journal
المؤلفون: G. G. Kаrmаzаnovsky, M. Y. Shantarevich, V. I. Stashkiv, A. Sh. Revishvili, Г. Г. Кармазановский, М. Ю. Шантаревич, В. И. Сташкив, А. Ш. Ревишвили
المصدر: Medical Visualization; Том 27, № 3 (2023); 84-93 ; Медицинская визуализация; Том 27, № 3 (2023); 84-93 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: радиомика, CT, MRI, texture analysis, radiomics, КТ, МРТ, текстурный анализ
وصف الملف: application/pdf
Relation: https://medvis.vidar.ru/jour/article/view/1372/830; Allemani C., Matsuda T., Di Carlo V. et al. CONCORD Working Group. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018; 391 (10125): 1023–1075. https://doi.org/10.1016/S0140-6736(17)33326-3; Hassanipour S., Vali M., Gaffari-Fam S. et al. The survival rate of hepatocellular carcinoma in Asian countries: a systematic review and meta-analysis. EXCLI J. 2020; 19: 108–130. https://doi.org/10.17179/excli2019-1842; Siegel R., Naishadham D., Jemal A. Cancer statistics. 2013. CA Cancer J. Clin. 2013; 63: 11–30. https://doi.org/10.3322/caac.21166; Кармазановский Г.Г., Шантаревич М.Ю. Обзор международных клинических рекомендаций и результатов клинических исследований по диагностике гепатоцеллюлярного рака за 2014–2020 годы. Анналы хирургической гепатологии. 2021; 26 (1): 12–24. https://doi.org/10.16931/1995-5464.2021112-24.; Ломовцева К.Х. Дифференциальная диагностика образований печени солидной структуры: роль диффузионно-взвешенных изображений и гепатоспецифичных контрастных средств: Дис. . канд. мед. наук. М., 2018. 117 с.; Ломовцева К.Х., Кармазановский Г.Г. Диффузионновзвешенные изображения при очаговой патологии печени: обзор литературы. Медицинская визуализация. 2015; 6: 50–60.; Semaan S., ViettiVioli N., Lewis S. et al. Hepatocellular carcinoma detection in liver cirrhosis: diagnostic performance of contrast-enhanced CT vs. MRI with extracellular contrast vs. gadoxetic acid. Eur. Radiol. 2020; 30 (2): 1020–1030. https://doi.org/10.1007/s00330-019-06458-4. PMID: 31673837; An C., Lee C.H., Byun J.H. et al. Intraindividual comparison between gadoxetate-enhanced magnetic resonance imaging and dynamic computed tomography for characterizing focal hepatic lesions: a multicenter, multireader study. Korean J. Radiol. 2019; 20 (12): 1616–1626. https://doi.org/10.3348/kjr.2019.0363; Omata M., Cheng A.L., Kokudo N. et al. Asia-Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatol. Int. 2017; 11 (4): 317–370. https://doi.org/10.1007/s12072-017-9799-9. PMID: 28620797; PMCID: PMC5491694; Гайдель А.В., Зельтер, П.М., Капишников А.В., Храмов А.Г. Возможности текстурного анализа компьютерных томограмм в диагностике хронической обструктивной болезни. Компьютерная оптика. 2014; 38 (4): 843–850.; Гайдель А.В., Первушкин С.С. Исследование текстурных признаков для диагностики заболеваний костной ткани по рентгеновским изображениям. Компьютерная оптика. 2013; 37.1: 113–119.; Park H.J., Park B., Lee S.S. Radiomics and Deep Learning: Hepatic Applications. Korean J. Radiol. 2020; 21 (4): 387–401. https://doi.org/10.3348/kjr.2019.0752; Ganeshan B., Miles K.A. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013; 13 (1): 140–149. https://doi.org/10.1102/1470-7330.2013.0015. PMID: 23545171; PMCID: PMC3613789.; Liang W., Shao J., Liu W. et al. Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models. Front. Oncol. 2020; 10: 564307. https://doi.org/10.3389/fonc.2020.564307; Liu X., Jiang H., Chen J. et al. Gadoxetic acid disodiumenhanced magnetic resonance imaging outperformed multidetector computed tomography in diagnosing small hepatocellular carcinoma: A meta-analysis. Liver Transpl. 2017; 23 (12): 1505–1518. https://doi.org/10.1002/lt.24867; Oh J., Lee J.M., Park J. et al. Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and DiseaseFree Survival. Korean J. Radiol. 2019; 20 (4): 569–579. https://doi.org/10.3348/kjr.2018.0501; Wu M., Tan H., Gao F. et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur. Radiol. 2019; 29 (6): 2802–2811. https://doi.org/10.1007/s00330-018-5787-2; Шантаревич М.Ю., Кармазановский Г.Г. Применение текстурного анализа КТ и МР-изображений для определения степени дифференцировки гепатоцеллюлярного рака и его дифференциальной диагностики: обзор литературы. Research'n Practical Medicine Journal. 2022; 9 (3): 129–144.; WHO Classification of Tumours 5th Edition Digestive System Tumours by WHO Classification of Tumours Editorial Board; Nioche C., Orlhac F., Boughdad S. et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018; 78 (16): 4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125; Mao B., Zhang L., Ning P. et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur. Radiol. 2020; 30 (12): 6924–6932. https://doi.org/10.1007/s00330-020-07056-5; Liu X., Khalvati F., Namdar K. et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur. Radiol. 2021; 31 (1): 244–255. https://doi.org/10.1007/s00330-020-07119-7; Meng X.P., Wang Y.C., Zhou J.Y. et al. Comparison of MRI and CT for the prediction of microvascular invasion in solitary hepatocellular carcinoma based on a nonradiomics and radiomics method: which imaging modality is better? J. Magn. Reson. Imaging. 2021; 54 (2): 526–536. https://doi.org/10.1002/jmri.27575.; Hu H.T., Shan Q.Y., Chen S.L. et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol. Med. 2020; 125 (8): 697–705. https://doi.org/10.1007/s11547-020-01174-2; Shafiq-Ul-Hassan M., Zhang G.G., Latifi K. et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics. 201744 (3): 1050–1062. https://doi.org/10.1002/mp.12123; Sun R., Limkin E.J., Vakalopoulou M., et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018; 19 (9): 1180–1191. https://doi.org/10.1016/S1470-2045(18)30413-3; https://medvis.vidar.ru/jour/article/view/1372
-
12Academic Journal
المؤلفون: A. S. Maksimova, W. Yu. Ussov, T. A. Shelkovnikova, O. V. Mochula, N. I. Ryumshina, A. E. Sykhareva, K. V. Zavadovsky, А. С. Максимова, В. Ю. Усов, Т. А. Шелковникова, О. В. Мочула, Н. И. Рюмшина, А. Е. Сухарева, К. В. Завадовский
المصدر: The Siberian Journal of Clinical and Experimental Medicine; Том 38, № 3 (2023); 13-22 ; Сибирский журнал клинической и экспериментальной медицины; Том 38, № 3 (2023); 13-22 ; 2713-265X ; 2713-2927
مصطلحات موضوعية: текстурный анализ, myocarditis, myocardial infarction, cardiomyopathy, radiomics, textural analysis, миокардит, инфаркт миокарда, кардиомиопатия, радиомика
وصف الملف: application/pdf
Relation: https://www.sibjcem.ru/jour/article/view/1932/854; Mayerhoefer M.E., Materka A., Langs G., Häggström I., Szczypiński P., Gibbs P. et al. Introduction to Radiomics. J. Nucl. Med. 2020;61(4):488–495. DOI:10.2967/jnumed.118.222893.; van Timmeren J.E., Cester D., Tanadini-Lang S., Alkadhi H., Baessler B. Radiomics in medical imaging-“how-to” guide and critical refl ection. Insights Imaging. 2020;11(1):91. DOI:10.1186/s13244-020-00887-2.; Murray J.M., Kaissis G., Braren R., Kleesiek J. Wie funktioniert Radiomics? [A primer on radiomics]. Radiologe. 2020;60(1):32–41. (In German). DOI:10.1007/s00117-019-00617-w.; Avanzo M., Stancanello J., Pirrone G., Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–887. DOI:10.1007/s00066-020-01625-9.; Огнерубов Н.А., Шатов А.В., Шатов И.А. Радиогеномика и радиомика в диагностике злокачественных опухолей: обзор литературы. Вестник Тамбовского университета. Серия: Естественные и технические науки. 2017;22(6–2):1453–1460. DOI:10.20310/1810-0198-2017-22-6-1453-1460.; Литвин А.А., Буркин Д.А., Кропинов А.А., Парамзин Ф.Н. Радиомика и анализ текстур цифровых изображений в онкологии (обзор). Современные технологии в медицине. 2021;13(2):97–106. DOI:10.17691/stm2021.13.2.11.; Замятина К.А., Годзенко М.В., Кармазановский Г.Г., Ревишвили А.Ш. Радиомика при заболеваниях печени и поджелудочной железы. Обзор литературы. Анналы хирургической гепатологии. 2022;27(1):40–47. DOI:10.16931/1995-5464.2022-1-40-47.; Shur J.D., Doran S.J., Kumar S., Ap Dafydd D., Downey K., O’Connor J.P.B. et al. Radiomics in oncology: A practical guide. Radiographics. 2021;41(6):1717–1732. DOI:10.1148/rg.2021210037.; Salvatore C., Castiglioni I., Cerasa A. Radiomics approach in the neurodegenerative brain. Aging Clin. Exp. Res. 2021;33(6):1709–1711. DOI:10.1007/s40520-019-01299-z.; Feng Q., Ding Z. MRI Radiomics classification and prediction in Alzheimer’s disease and mild cognitive impairment: A review. Curr. Alzheimer Res. 2020;17(3):297–309. DOI:10.2174/1567205017666200303105016.; Pinamonti B., Picano E., Ferdeghini E.M., Lattanzi F., Slavich G., Landini L. et al. Quantitative texture analysis in two-dimensional echocardiography: application to the diagnosis of myocardial amyloidosis. J. Am. Coll. Cardiol. 1989;14(3):666–671. DOI:10.1016/0735-1097(89)90108-3.; Ferdeghini E.M., Pinamonti B., Picano E., Lattanzi F., Bussani R., Slavich G. et al. Quantitative texture analysis in echocardiography: application to the diagnosis of myocarditis. J. Clin. Ultrasound. 1991;19(5):263–270. DOI:10.1002/jcu.1870190503.; Lattanzi F., Bellotti P., Picano E., Chiarella F., Paterni M., Forni G. et al. Quantitative texture analysis in two-dimensional echocardiography: Application to the diagnosis of myocardial hemochromatosis. Echocardiography. 1996;13(1):9–20. DOI:10.1111/j.1540-8175.1996.tb00863.x.; Kagiyama N., Shrestha S., Cho J.S., Khalil M., Singh Y., Challa A. et al. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine. 2020;54:102726. DOI:10.1016/j.ebiom.2020.102726.; Amichi A., Laugier P. Quantitative texture analysis and transesophageal echocardiography to characterize the acute myocardial contusion. Open Med. Inform. J. 2009;3:13–18. DOI:10.2174/1874431100903010013.; Li L., Hu X., Tao X., Shi X., Zhou W., Hu H. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. Eur. J. Radiol. 2021;140:109769. DOI:10.1016/j.ejrad.2021.109769.; Shang J., Guo Y., Ma Y., Hou Y. Cardiac computed tomography radiomics: a narrative review of current status and future directions. Quant. Imaging Med. Surg. 2022;12(6):3436–3453. DOI:10.21037/qims-21-1022.; Yunus M.M., Mohamed Yusof A.K., Ab Rahman M.Z., Koh X.J., Sabarudin A., Nohuddin P.N.E. et al. Automated classification of atherosclerotic radiomics features in coronary computed tomography angiography (CCTA). Diagnostics (Basel). 2022;12(7):1660. DOI:10.3390/diagnostics12071660.; Oikonomou E.K., Williams M.C., Kotanidis C.P., Desai M.Y., Marwan M., Antonopoulos A.S. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 2019;40(43):3529–3543. DOI:10.1093/eurheartj/ehz592.; Hu G.Q., Ge Y.Q., Hu X.K., Wei W. Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors. BMC Med. Imaging. 2022;22(1):134. DOI:10.1186/s12880-022-00858-7.; Попов Е.В., Анашбаев Ж.Ж., Мальцева А.Н., Сазонова С.И. Радиомические характеристики текстурных изменений эпикардиальной жировой ткани при атеросклеротическом поражении коронарных артерий. Комплексные проблемы сердечно-сосудистых заболеваний. 2021;10(4):6–16. DOI:10.17802/2306-1278-2021-10-4-6-16.; Leiner T. Radiomics in cardiac MRI: Sisyphean struggle or close to the summit of Olympus? Radiol. Cardiothorac. Imaging. 2020;25;2(3):e200244. DOI:10.1148/ryct.2020200244.; Chang S., Han K., Suh Y.J., Choi B.W. Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur. Radiol. 2022;32(7):4361–4373. DOI:10.1007/s00330-022-08587-9.; Jang J., Ngo L.H., Mancio J., Kucukseymen S., Rodriguez J., Pierce P. et al. Reproducibility of segmentation-based myocardial radiomic features with cardiac MRI. Radiol. Cardiothorac. Imaging. 2020;2(3):e190216. DOI:10.1148/ryct.2020190216.; Baessler B., Mannil M., Maintz D., Alkadhi H., Manka R. Texture analysis and machine learning of noncontrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results. Eur. J. Radiol. 2018;102:61–67. DOI:10.1016/j.ejrad.2018.03.013.; Alis D., Guler A., Yergin M., Asmakutlu O. Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI. Diagn. Interv. Imaging. 2020;101:137–146. DOI:10.1016/j.diii.2019.10.005.; Baessler B., Luecke C., Lurz J., Klingel K., von Roeder M., de Waha S. et al. Cardiac MRI texture analysis of T1 and T2 maps in patients with infarctlike acute myocarditis. Radiology. 2018;289(2):357–365. DOI:10.1148/radiol.2018180411.; Hassani C., Saremi F., Varghese B.A, Duddalwar V. Myocardial radiomics in cardiac MRI. AJR Am. J. Roentgenol. 2020;214(3):536–545. DOI:10.2214/AJR.19.21986.; Koçak B., Durmaz E.Ş., Ateş E., Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn. Interv. Radiol. 2019;25(6):485–495. DOI:10.5152/dir.2019.19321.; Buch K., Kuno H., Qureshi M.M., Li B., Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J. Appl. Clin. Med. Phys. 2018;19(6):253–264. DOI:10.1002/acm2.12482.; Florez E., Fatemi A., Claudio P.P., Howard C.M. Emergence of radiomics: Novel methodology identifying imaging biomarkers of disease in diagnosis, response, and progression. SM J. Clin. Med. Imaging. 2018;4(1):1019.; Scapicchio C., Gabelloni M., Barucci A., Cioni D., Saba L., Neri E. A deep look into radiomics. Radiol. Med. 2021;126(10):1296–1311. DOI:10.1007/s11547-021-01389-x.; Rizzo S., Botta F., Raimondi S., Origgi D., Fanciullo C., Morganti A.G. et al. Radiomics: the facts and the challenges of image analysis. Eur. Radiol. Exp. 2018;2:36. DOI:10.1186/s41747-018-0068-z.; Aerts H., Velazquez E., Leijenaar R.T.H., Parmar C., Grossmann P., Carvalho S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014;5:4006. DOI:10.1038/ncomms5006.; Parmar C., Grossmann P., Bussink J., Lambin P., Aerts H.J.W.L. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 2015;5:13087. DOI:10.1038/srep13087.; Jolliffe I. Principal component analysis. In: Encyclopedia of Statistics in Behavioral Science. Wiley StatsRef: Stastistics Reference Online. 2005:501. DOI:10.1002/0470013192.bsa501.; Rau A., Soschynski M., Taron J., Ruile P., Schlett C.L., Bamberg F. et al. Künstliche Intelligenz und Radiomics: Stellenwert in der kardialen MRT [Artificial intelligence and radiomics: Value in cardiac MRI]. Radiologie (Heidelb.). 2022;62(11):947–953. (In German). DOI:10.1007/s00117-022-01060-0.; Арутюнов Г.П., Палеев Ф.Н., Моисеева О.М., Драгунов Д.О., Соколова А.В., Арутюнов А.Г. и др. Миокардиты у взрослых. Клинические рекомендации 2020. Российский кардиологический журнал. 2021;26(11):4790. DOI:10.15829/1560-4071-2021-4790.; Baessler B., Luecke C., Lurz J., Klingel K., Das A., von Roeder M. et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure. Radiology. 2019:292(3):608–617. DOI:10.1148/radiol.2019190101.; Di Noto T., von Spiczak J., Mannil M., Gantert E., Soda P., Manka R. et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: Comparison with subjective visual analysis. Radiol. Cardiothorac. Imaging. 2019;1(5):e180026. DOI:10.1148/ryct.2019180026.; McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Böhm M. et al. ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021;42(36):3599–3726. DOI:10.1093/eurheartj/ehab368.; Belloni E., De Cobelli F., Esposito A., Mellone R., Perseghin G., Canu T. et al. MRI of cardiomyopathy. AJR Am. J. Roentgenol. 2008;191(6):1702–1710. DOI:10.2214/AJR.07.3997.; Amano Y., Yanagisawa F., Omori Y., Suzuki Y., Ando C., Yamamoto H. et al. Detection of myocardial tissue alterations in hypertrophic cardiomyopathy using texture analysis of T2-weighted short inversion time inversion recovery magnetic resonance imaging. J. Comput. Assist. Tomogr. 2020;44(3):341–345. DOI:10.1097/RCT.0000000000001007.; Schofield R., Ganeshan B., Fontana M., Nasis A., Castelletti S., Rosmini S. et al. Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy. Clin. Radiol. 2019;74(2):140–149. DOI:10.1016/j.crad.2018.09.016.; Izquierdo C., Casas G., Martin-Isla C., Campello V.M., Guala A., Gkontra P. et al. Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance. Front. Cardiovasc. Med. 2021;8:764312. DOI:10.3389/fcvm.2021.764312.; Neisius U., El-Rewaidy H., Kucukseymen S., Tsao C.W., Mancio J. et al. Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar. J. Magn. Reson. Imaging. 2020;52(3):906–919. DOI:10.1002/jmri.27048.; Wang J., Yang F., Liu W., Sun J., Han Y., Li D. et al. Radiomic analysis of native T1 mapping images discriminates between MYH7 and MYB-PC3-related hypertrophic cardiomyopathy. J. Magn. Reson. Imaging. 2020;52(6):1714–1721. DOI:10.1002/jmri.27209.; Spadarella G., Perillo T., Ugga L., Cuocolo R. Radiomics in cardiovascular disease imaging: from pixels to the heart of the problem. Curr. Cardiovasc. Imaging Rep. 2022;15:11–21. DOI:10.1007/s12410-022-09563-z.; Larroza A., López-Lereu M.P., Monmeneu J.V., Gavara J., Chorro F.J., Bodí V. et al. Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction. Med. Phys. 2018;45(4):1471–1480. DOI:10.1002/mp.12783.; Chen B., An D., He J., Wu C.-W., Yue T., Wu R. et al. Myocardial extracellular volume fraction radiomics analysis for differentiation of reversible versus irreversible myocardial damage and prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction. Eur. Radiol. 2021;31:504–514. DOI:10.1007/s00330-020-07117-9.; Eftestøl T., Woie L., Engan K., Kvaløy J.T., Nilsen D.W., Ørn S. Texture analysis to assess risk of serious arrhythmias after myocardial infarction. In: Computing in Cardiology IEEE. Krakow, Poland; 2012:365–368. URL: https://ieeexplore.ieee.org/abstract/document/6420406 (06.07.2023).; Engan K., Eftestol T., Orn S., Kvaloy J.T., Woie L. Exploratory data analysis of image texture and statistical features on myocardium and infarction areas in cardiac magnetic resonance images. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010;2010:5728–5731. DOI:10.1109/IEMBS.2010.5627866.; Ma Q., Ma Y., Yu T., Sun Z., Hou Y. Radiomics of non-contrast-enhanced T1 mapping: diagnostic and predictive performance for myocardial injury in acute ST-segment-elevation myocardial infarction. Korean J. Radiol. 2021;22(4):535–546. DOI:10.3348/kjr.2019.0969.; Raisi-Estabragh Z., Gkontra P., Jaggi A., Cooper J., Augusto J., Bhuva A.N. et al. Repeatability of cardiac magnetic resonance radiomics: A multi-centre multi-vendor test-retest study. Front. Cardiovasc. Med. 2020;7:586236. DOI:10.3389/fcvm.2020.586236.; Park J.E., Park S.Y., Kim H.J., Kim H.S. Reproducibility and generalizability in radiomics modeling: Possible strategies in radiologic and statistical perspectives. Korean J. Radiol. 2019;20(7):1124–1137. DOI:10.3348/kjr.2018.0070.; Park S.H., Han K. Methodologic Guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286(3):800–809. DOI:10.1148/radiol.2017171920.; Varghese B.A., Cen S.Y., Hwang D.H., Duddalwar V.A. Texture analysis of imaging: What radiologists need to know. AJR Am. J. Roentgenol. 2019;212(3):520–528. DOI:10.2214/AJR.18.20624.; Kumar V., Gu Y., Basu S., Berglund A., Eschrich S.A., Schabath M.B. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012;30(9):1234–1248. DOI:10.1016/j.mri.2012.06.010.; Amano Y., Suzuki Y., Yanagisawa F., Omori Y., Matsumoto N. Relationship between extension or texture features of late gadolinium enhancement and ventricular tachyarrhythmias in hypertrophic cardiomyopathy. Biomed. Res. Int. 2018;2018:4092469. DOI:10.1155/2018/4092469.; https://www.sibjcem.ru/jour/article/view/1932
-
13Academic Journal
المؤلفون: E. V. Popov, Y. N. Ilyushenkova, A. N. Repin, K. V. Zavadovsky, S. I. Sazonova, Е. В. Попов, Ю. Н. Ильюшенкова, А. Н. Репин, К. В. Завадовский, С. И. Сазонова
المساهمون: the study was carried out in the framework of the state assignment., исследование выполнено в рамках государственного задания.
المصدر: The Siberian Journal of Clinical and Experimental Medicine; Том 38, № 3 (2023); 143-152 ; Сибирский журнал клинической и экспериментальной медицины; Том 38, № 3 (2023); 143-152 ; 2713-265X ; 2713-2927
مصطلحات موضوعية: атеросклероз, texture analysis, acute coronary syndrome, atherosclerosis, текстурный анализ, острый коронарный синдром
وصف الملف: application/pdf
Relation: https://www.sibjcem.ru/jour/article/view/1956/870; Ferrari R., Rosano G. 2019 guidelines for the diagnosis and management of chronic coronary syndromes: congratulations and criticism [published correction appears in: Eur. Heart J. Cardiovasc. Pharmacother. 2021;7(3):179]. Eur. Heart J. Cardiovasc. Pharmacother. 2020;6(5):331–332. DOI:10.1093/ehjcvp/pvaa006.; Меркулова И.Н., Шария М.А., Миронов В.М., Шабанова М.С., Веселова Т.Н., Гаман С.А. и др. Возможности компьютерной томографии в выявлении атеросклеротических бляшек высокого риска у больных с острым коронарным синдромом без подъема сегмента ST: сопоставление с внутрисосудистым ультразвуковым исследованием. Кардиология. 2020;60(12):64–75. DOI:10.18087/cardio.2020.12. n1304.; Mayerhoefer M.E., Materka A., Langs G., Häggström I., Szczypiński P., Gibbs P. et al. Introduction to Radiomics. J. Nucl. Med. 2020;61(4):488–495. DOI:10.2967/jnumed.118.222893.; Попов Е.В., Анашбаев Ж.Ж., Мальцева А.Н., Сазонова С.И. Радиомические характеристики текстурных изменений эпикардиальной жировой ткани при атеросклеротическом поражении коронарных артерий. Комплексные проблемы сердечно-сосудистых заболеваний. 2021;10(4):6–16. DOI:10.17802/2306-1278-2021-10-4-6-16.; Cheng K., Lin A., Yuvaraj J., Nicholls S.J., Wong D.T.L. Cardiac computed tomography radiomics for the non-invasive assessment of coronary inflammation. Cells. 2021;10(4):879. DOI:10.3390/cells10040879.; Kolossváry M., Jávorszky N., Karády J., Vecsey-Nagy M., Dávid T.Z., Simon J. et al. Effect of vessel wall segmentation on volumetric and radiomic parameters of coronary plaques with adverse characteristics. J. Cardiovasc. Comput. Tomogr. 2021;15(2):137–145. DOI:10.1016/j.jcct.2020.08.001.; Kolossváry M., Karády J., Szilveszter B., Kitslaar P., Hoffmann U., Merkely B. et al. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign. Circ. Cardiovasc. Imaging. 2017;10(12):e006843. DOI:10.1161/CIRCIMAGING.117.006843.; Kolossváry M., Park J., Bang J.I., Zhang J., Lee J.M., Paeng J.C. et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. Eur. Heart J. Cardiovasc. Imaging. 2019;20(11):1250–1258. DOI:10.1093/ehjci/jez033.; Kolossváry M., Karády J., Kikuchi Y., Ivanov A., Schlett C.L., Lu M.T. et al. Radiomics versus Visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: An ex vivo study. Radiology. 2019;293(1):89–96. DOI:10.1148/radiol.2019190407.; Kolossváry M., De Cecco C.N., Feuchtner G., Maurovich-Horvat P. Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. J. Cardiovasc. Comput. Tomogr. 2019;13(5):274–280. DOI:10.1016/j.jcct.2019.04.007.; Oikonomou E.K., Siddique M., Antoniades C. Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease. Cardiovasc. Res. 2020;116(13):2040–2054. DOI:10.1093/cvr/cvaa021.; Oikonomou E.K., Williams M.C., Kotanidis C.P., Desai M.Y., Marwan M., Antonopoulos A.S. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur. Heart J. 2019;40(43):3529–3543. DOI:10.1093/eurheartj/ehz592.; Ilyushenkova J., Sazonova S., Popov E., Zavadovsky K., Batalov R., Archakov E. et al. Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation. J. Arrhythm. 2022;38(5):682–693. DOI:10.1002/joa3.12760.; Zwanenburg A., Vallières M., Abdalah M.A., Aerts H.J.W.L., Andrearczyk V., Apte A. et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–338. DOI:10.1148/radiol.2020191145.; Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.C., Pujol S. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging. 2012;30(9):1323–1341. DOI:10.1016/j.mri.2012.05.001.; Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., van Stiphout R.G., Granton P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012;48(4):441–446. DOI:10.1016/j.ejca.2011.11.036.; Hamm C.W., Bassand J.P., Agewall S., Bax J., Boersma E., Bueno H. et al. ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: The Task Force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur. Heart J. 2011;32(23):2999–3054. DOI:10.1093/eurheartj/ehr236.; Writing Committee Members, Gulati M., Levy P.D., Mukherjee D., Amsterdam E., Bhatt D.L. et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the evaluation and diagnosis of chest pain: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J. Cardiovasc. Comput. Tomogr. 2022;16(1):54–122. DOI:10.1016/j.jcct.2021.11.009.; Antonopoulos A.S., Sanna F., Sabharwal N., Thomas S., Oikonomou E.K., Herdman L. et al. Detecting human coronary inflammation by imaging perivascular fat. Sci. Transl. Med. 2017;9(398):eaal2658. DOI:10.1126/scitranslmed.aal2658.; Lee S., Han K., Suh Y.J. Quality assessment of radiomics research in cardiac CT: a systematic review. Eur. Radiol. 2022;32(5):3458–3468. DOI:10.1007/s00330-021-08429-0.; https://www.sibjcem.ru/jour/article/view/1956
-
14Academic Journal
المؤلفون: V. Stashkiv I., D. Kalinin V., G. Karmazanovsky G., В. Сташкив И., Д. Калинин В., Г. Кармазановский Г.
المصدر: Diagnostic radiology and radiotherapy; Том 12, № 4 (2021); 15-22 ; Лучевая диагностика и терапия; Том 12, № 4 (2021); 15-22 ; 2079-5343
مصطلحات موضوعية: HCC, texture analysis, radiomics, MRI, гепатоцеллюлярный рак, текстурный анализ, радиомика, МРТ
وصف الملف: application/pdf
Relation: https://radiag.bmoc-spb.ru/jour/article/view/652/503; Хубулава Г.Г., Гаврилов Е.К., Шишкевич А.Н., Ларин И.А., Алборов Ю.Р., Садовой С.В. Диагностика и хирургическое лечение восходящих глубоких флеботромбозов нижних конечностей и таза // Вестник хирургии. 2018. Т. 177, № 2. С. 46–51.; Медведев А.П., Максимов А.Л., Немирова С.В., Пичугин В.В., Демарин О.И., Калинина М.Л., Козина М.Б., Чигинев В.А. Опыт диагностики и хирургического лечения тромбоэмболии легочной артерии у беременных // Современные технологии в медицине. 2017. № 9 (4). С. 143– 155.; Российские клинические рекомендации по диагностике, лечению, профилактике венозных тромбоэмболических осложнений // Флебология. 2015. Т. 9, № 4 (2). С. 4–46.; Konstantinides S.V., Torbicki A., Agnelli G., Danchin N., Fitzmaurice D., Galiè N., Gibbs J.S. 2014 ESC Guidelines on the diagnosis and management of acute pulmonary embolism: the task force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC) // Eur. Heart J. 2014. Vol. 35, No. 43. P. 3033–3069. doi:10.1093/eurheartj/ehu283.; Юдин А.Л., Учеваткин А.А., Афанасьева Н.И., Юматова Е.А., Кулагин А.Л. Роль мультидетекторной компьютерной томографии в диагностике венозной тромбоэмболии // Российский медицинский журнал. 2015. № 21 (1). С. 40–43.; Mean M., Tritschler T., Limacher A., Breault S., Rodondi N., Aujesky D., Qanadli SD. Association between computed tomography obstruction index and mortality in elderly patients with acute pulmonary embolism: A prospective validation study // PLoS ONE. 2017. Vol. 12, No. 6. P. 1–13 . doi:10.1371/journal.pone.0179224.; John G., Marti C., Poletti P.A., Perrier A. Hemodynamic Indexes Derived from Computed Tomography Angiography to Predict Pulmonary Embolism Related Mortality // BioMed Research International. 2014. P. 8. doi:10.1155/2014/363756; Сухова М.Б. Острая массивная тромбоэмболия легочной артерии. Современный взгляд на анализ результатов МСКТ-исследования // Современные стандарты лучевых исследований и принципы построения заключений: руководство для врачей / под ред. Т.Н.Трофимовой. СПб., 2021. С. 440.; Синельников Р.Д., Синельников Я.Р. Атлас анатомии человека. В 4 т. Т. 2. Учение о внутренностях. Спланхнология. 2-е изд., стер. М., 1996. С. 157–160.; Aviram G., Rogowski O., Gotler Y., Bendler A., Steinvil A., Goldin Y., Graif M., Berliner S. Real Time Risk Stratification of Patients with Acute Pulmonary Embolism by Grading the Reflux of Contrast into the Inferior Vena Cava on Computerized Tomographic Pulmonary Angiography // Journal of Thrombosis and Haemostasis. 2008. Vol. 6, No. 9. P. 1488–1493. doi:10.1111/j.1538–7836.2008.03079.; Yeh B.M., Kurzman P., Foster E., Qayyum A., Joe B., Coakley F. Clinical relevance of retrograde inferior vena cava or hepatic vein opacification during contrast-enhanced CT // Am. J. Roentgenol. 2004. Vol. 183, No. 5. P. 1227– 1232. doi:10.2214/ajr.183.5.1831227.; Казарян А.М., Акопов А.Л., Росок Б., Постриганова Н.Д., Эдвин Б. Российская редакция классификации осложнений в хирургии // Вестник хирургии им. И.И.Грекова. 2014. Т. 173, № 2. С. 86–91; Тюрин В.П., Пронин А.Г. Сопоставление критериев стратификации риска смерти при тромбоэмболии легочной артерии с учетом объема поражения легочных артерий // Международный журнал сердца и сосудистых заболеваний. 2018. Т. 6, № 18. С. 36–47.; Савельев В.С., Яблоков Е.Г., Кириенко А.И. Тромбоэмболия легочных артерий. М.: Медицина, 1979. С. 264 [Savelyev V.S., Yablokov E.G., Kiriyenko A.I. Pulmonary embolism. Moscow: Publishing house Medicine, 1979, рр. 264 (In Russ.)].; Хубулава Г.Г., Гаврилов Е.К., Тарасов В.А. Ближайшие и отдаленные результаты открытой тромбэктомии из глубоких вен нижних конечностей при флотирующих флеботробозах с предварительной имплантацией кавафильтра и без // Анналы хирургии. 2016. Т. 21, № 1–2. С. 139–144.; Алехин М.Н. Возможности и ограничения эхокардиографии в оценке давления в легочной артерии и правых камерах сердца // Ультразвуковая и функциональная диагностика. 2012. № 6. С. 106–116.; Юдин А.Л., Учеваткин А.А., Афанасьева Н.И., Юматова Е.А., Рудая А.И. Бронхиальные артерии — анатомические особенности и пути ремоделирования // Лучевая диагностика и терапия. 2015. № 1 (6). С. 32–38.; https://radiag.bmoc-spb.ru/jour/article/view/652
-
15Academic Journal
المصدر: Research and Practical Medicine Journal; Том 9, № 3 (2022); 129-144 ; Research'n Practical Medicine Journal; Том 9, № 3 (2022); 129-144 ; 2410-1893 ; 10.17709/2410-1893-2022-9-3
مصطلحات موضوعية: дифференциальная диагностика, texture analysis, radiomics, MRI, CT, histologic grade, differential diagnosis, текстурный анализ, радиомика, МРТ, КТ, степень гистологической дифференцировки
وصف الملف: application/pdf
Relation: https://www.rpmj.ru/rpmj/article/view/783/501; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/599; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/600; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/601; Состояние онкологической помощи населению России в 2019 году. Под ред. Каприна А. Д., Старинского В. В, Шахзадовой А. О. М.: МНИОИ им. П. А. Герцена − филиал ФГБУ «НМИЦ радиологии» Минздрава России, 2020, 252 с.; Hanna RF, Miloushev VZ, Tang A, Finklestone LA, Brejt SZ, Sandhu RS, et al. Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY). 2016 Jan;41(1):71–90. https://doi.org/10.1007/s00261-015-0592-8; An C, Lee CH, Byun JH, Lee MH, Jeong WK, Choi SH, et al. Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study. Korean J Radiol. 2019 Dec;20(12):1616–1626. https://doi.org/10.3348/kjr.2019.0363; Martins-Filho SN, Paiva C, Azevedo RS, Alves VAF. Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne). 2017;4:193. https://doi.org/10.3389/fmed.2017.00193; Okusaka T, Okada S, Ueno H, Ikeda M, Shimada K, Yamamoto J, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathologic features. Cancer. 2002 Nov 1;95(9):1931–1937. https://doi.org/10.1002/cncr.10892; Nishie A, Yoshimitsu K, Okamoto D, Tajima T, Asayama Y, Ishigami K, et al. CT prediction of histological grade of hypervascular hepatocellular carcinoma: utility of the portal phase. Jpn J Radiol. 2013 Feb;31(2):89–98. https://doi.org/10.1007/s11604-012-0149-5; Ломовцева К. Х. Дифференциальная диагностика образований печени солидной структуры: роль диффузионно-взвешенных изображений и гепатоспецифичных контрастных средств: Дисс. … канд. мед. наук. М., 2018, 140 с.; Jeong WK, Jamshidi N, Felker ER, Raman SS, Lu DS. Radiomics and radiogenomics of primary liver cancers. Clin Mol Hepatol. 2019 Mar;25(1):21–29. https://doi.org/10.3350/cmh.2018.1007; Oh J, Lee JM, Park J, Joo I, Yoon JH, Lee DH, et al. Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean J Radiol. 2019 Apr;20(4):569–579. https://doi.org/10.3348/kjr.2018.0501; Mao B, Zhang L, Ning P, Ding F, Wu F, Lu G, et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol. 2020 Dec;30(12):6924–6932. https://doi.org/10.1007/s00330-020-07056-5; Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, et al. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol. 2021;11:660509. https://doi.org/10.3389/fonc.2021.660509; Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2019 Jun;29(6):2802–2811. https://doi.org/10.1007/s00330-018-5787-2; Geng Z, Zhang Y, Wang S, Li H, Zhang C, Yin S, et al. Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features. Magn Reson Med Sci. 2021 Sep 1;20(3):253–263. https://doi.org/10.2463/mrms.mp.2020-0060; Chen W, DelProposto Z, Liu W, Kassir M, Wang Z, Zhao J, et al. Susceptibility-weighted imaging for the noncontrast evaluation of hepatocellular carcinoma: a prospective study with histopathologic correlation. PLoS One. 2014;9(5):e98303. https://doi.org/10.1371/journal.pone.0098303; Yang S, Lin J, Lu F, Han Z, Fu C, Gu H. Use of Ultrasmall Superparamagnetic Iron Oxide Enhanced Susceptibility Weighted Imaging and Mean Vessel Density Imaging to Monitor Antiangiogenic Effects of Sorafenib on Experimental Hepatocellular Carcinoma. Contrast Media Mol Imaging. 2017;2017:9265098. https://doi.org/10.1155/2017/9265098; Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017 May;45(5):1476–1484. https://doi.org/10.1002/jmri.25454; Feng M, Zhang M, Liu Y, Jiang N, Meng Q, Wang J, et al. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study. BMC Cancer. 2020 Jun 30;20(1):611. https://doi.org/10.1186/s12885-020-07094-8; Yang X, Yuan C, Zhang Y, Wang Z. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study. Medicine (Baltimore). 2021 May 14;100(19):e25838. https://doi.org/10.1097/MD.0000000000025838; Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020 Jan;30(1):558–570. https://doi.org/10.1007/s00330-019-06347-w; Zhong X, Tang H, Lu B, You J, Piao J, Yang P, et al. Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging. Front Oncol. 2019;9:1382. https://doi.org/10.3389/fonc.2019.01382; Zhong X, Guan T, Tang D, Li J, Lu B, Cui S, et al. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol. 2021 Apr 7;21(1):155. https://doi.org/10.1186/s12876-021-01710-y; Raman SP, Schroeder JL, Huang P, Chen Y, Coquia SF, Kawamoto S, et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial fre quency measurements--a work in progress. J Comput Assist Tomogr. 2015 Jun;39(3):383–395. https://doi.org/10.1097/RCT.0000000000000217; Stocker D, Marquez HP, Wagner MW, Raptis DA, Clavien PA, Boss A, et al. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon. 2018 Nov;4(11):e00987. https://doi.org/10.1016/j.heliyon.2018.e00987; Wu J, Liu A, Cui J, Chen A, Song Q, Xie L. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images. BMC Med Imaging. 2019 Mar 11;19(1):23. https://doi.org/10.1186/s12880-019-0321-9; Nie P, Yang G, Guo J, Chen J, Li X, Ji Q, et al. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver. Cancer Imaging. 2020 Feb 24;20(1):20. https://doi.org/10.1186/s40644-020-00297-z; Nie P, Wang N, Pang J, Yang G, Duan S, Chen J, et al. CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver. Acad Radiol. 2021 Jun;28(6):799–807. https://doi.org/10.1016/j.acra.2020.04.027; Song S, Li Z, Niu L, Zhou X, Wang G, Gao Y, et al. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification. Clin Radiol. 2019 Aug;74(8):653.e11–653.e18. https://doi.org/10.1016/j.crad.2019.05.010; Oyama A, Hiraoka Y, Obayashi I, Saikawa Y, Furui S, Shiraishi K, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep. 2019 Jun 19;9(1):8764. https://doi.org/10.1038/s41598-019-45283-z; Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017 Jul 13;17(1):42. https://doi.org/10.1186/s12880-017-0212-x; Liang W, Shao J, Liu W, Ruan S, Tian W, Zhang X, et al. Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models. Front Oncol. 2020;10:564307. https://doi.org/10.3389/fonc.2020.564307; Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2021 Jan;31(1):244–255. https://doi.org/10.1007/s00330-020-07119-7; Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015 Nov;50(11):757–765. https://doi.org/10.1097/RLI.0000000000000180; Hu HT, Shan QY, Chen SL, Li B, Feng ST, Xu EJ, et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med. 2020 Aug;125(8):697–705. https://doi.org/10.1007/s11547-020-01174-2; Mackin D, Ger R, Dodge C, Fave X, Chi PC, Zhang L, et al. Effect of tube current on computed tomography radiomic features. Sci Rep. 2018 Feb 5;8(1):2354. https://doi.org/10.1038/s41598-018-20713-6; Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020 Apr;21(4):387–401. https://doi.org/10.3348/kjr.2019.0752; Li Y, Tan G, Vangel M, Hall J, Cai W. Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study. Quant Imaging Med Surg. 2020 Sep;10(9):1775–1785. https://doi.org/10.21037/qims-19-921; Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017 Mar;44(3):1050–1062. https://doi.org/10.1002/mp.12123; Leijenaar RTH, Nalbantov G, Carvalho S, van Elmpt WJC, Troost EGC, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015 Aug 5;5:11075. https://doi.org/10.1038/srep11075; Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013 Feb;82(2):342–348. https://doi.org/10.1016/j.ejrad.2012.10.023; Park HJ, Kim JH, Choi SY, Lee ES, Park SJ, Byun JY, et al. Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings. AJR Am J Roentgenol. 2017 Oct;209(4):W211–W220. https://doi.org/10.2214/AJR.16.17398; Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020 Apr;93(1108):20190948. https://doi.org/10.1259/bjr.20190948; Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328–338. https://doi.org/10.1148/radiol.2020191145; https://www.rpmj.ru/rpmj/article/view/783
-
16Academic Journal
المؤلفون: D. Gorduladze N., E. Sirota S., L. Rapoport M., V. Gridin N., D. Tsarichenko G., I. Kuznetsov A., P. Bochkaryov V., Yu. Alyaev G., Д. Гордуладзе Н., Е. Сирота С., Л. Рапопорт М., В. Гридин Н., Д. Цариченко Г., И. Кузнецов А., П. Бочкарев В., Ю. Аляев Г.
المساهمون: The work was performed within the framework of the theme No. 0071-2019-0001., Работа выполнена в рамках темы № 0071-2019-0001.
المصدر: Cancer Urology; Том 17, № 4 (2021); 129-135 ; Онкоурология; Том 17, № 4 (2021); 129-135 ; 1996-1812 ; 1726-9776
مصطلحات موضوعية: texture analysis, machine learning, radiomics, 5P medicine, multispiral computed tomography, magnetic resonance imaging, renal tumor, текстурный анализ, машинное обучение, радиомика, 5П-медицина, мультиспиральная компьютерная томография, магнитно-резонансная томография, опухоль почки
وصف الملف: application/pdf
Relation: https://oncourology.abvpress.ru/oncur/article/view/1436/1335; Wilhelm Konrad Roentgen-The Centennial of His Birth-Semicentennial of the X-Rays. JAMA 2020;323(15):1512. DOI:10.1001/jama.2019.13400.; Hounsfield G.N. Computerized transverse axial scanning (tomography): Part I. Description of system 1975. Br J Radiol 1973;68(815):H166-72.; Pincock S. US and UK researchers share Nobel prize. Paul C. Lauterbur and Peter Mansfield share award for seminal work on MRI. Lancet 2003;362(9391):1203. DOI:10.1016/s0140-6736(03)14557-6.; Davnall F., Yip C.S.P., Ljungqvist G. et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2012;3(6):573-89. DOI:10.1007/s13244-012-0196-6.; Haralick R.M., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;SMC-3(6):610-21. DOI:10.1109/TSMC.1973.4309314.; Aerts H.J.W.L. The potential of radiomic-based phenotyping in precisionmedicine a review. JAMA Oncol 2016;2(12):1636-42. DOI:10.1001/jamaoncol.2016.2631.; Lubner M.G., Smith A.D., Sandrasegaran K. et al. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 2017;37(5):1483-503. DOI:10.1148/rg.2017170056.; Blobel B., Ruotsalainen P., Brochhausen M. et al. Autonomous systems and artificial intelligence in healthcare transformation to 5p medicine - ethical challenges. Stud Health Technol Inform 2020;270:1089-93. DOI:10.3233/SHTI200330.; DeCastro G.J., McKiernan J.M. Epidemiology, clinical staging, and presentation of renal cell carcinoma. Urol Clin North Am 2008;35(4):581-92. DOI:10.1016/j.ucl.2008.07.005.; Frank I., Blute M.L., Cheville J.C. et al. Solid renal tumors: an analysis of pathological features related to tumor size. J Urol 2003;170(6 Pt 1):2217-20. DOI:10.1097/01.ju.0000095475.12515.5e.; Zhou L., Zhang Z., Chen Y.C. et al. A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl Oncol 2019;12(2):292-300. DOI:10.1016/j.tranon.2018.10.012.; Said D., Hectors S.J., Wilck E. et al. Characterization of solid renal neoplasms using MRI-based quantitative radiomics features. Abdom Radiol 2020;45(9):2840-50. DOI:10.1007/s00261-020-02540-4.; Uhlig J., Biggemann L., Nietert M.M. et al. Discriminating malignant and benign clinical T1 renal masses on computed tomography: a pragmatic radiomics and machine learning approach. Medicine 2020;99(16):e19725. DOI:10.1097/MD.0000000000019725.; Yap F.Y., Varghese B.A., Cen S.Y. et al. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses. Eur Radiol 2021;31(2):1011-21. DOI:10.1007/s00330-020-07158-0.; Deng Y., Soule E., Samuel A. et al. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019;29(12):6922-9. DOI:10.1007/s00330-019-06260-2.; Erdim C., Yardimci A.H., Bektas C.T. et al. Prediction of benign and malignant solid renal masses: machine learning-based CT texture analysis. Acad Radiol 2020;27(10):1422-9. DOI:10.1016/j.acra.2019.12.015.; Xi I.L., Zhao Y., Wang R. et al. Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging. Clin Cancer Res 2020;26(8):1944-52. DOI:10.1158/1078-0432.CCR-19-0374.; Znaor A., Lortet-Tieulent J., Laversanne M. et al. International variations and trends in renal cell carcinoma incidence and mortality. Eur Urol 2015;67(3):519-30. DOI:10.1016/j.eururo.2014.10.002.; Cheville J.C., Lohse C.M., Zincke H. et al. Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma. Am J Surg Pathol 2003;27(5):612-24. DOI:10.1097/00000478-200305000-00005.; Yu H.S., Scalera J., Khalid M. et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol 2017;42(10):2470-8. DOI:10.1007/s00261-017-1144-1.; Zhang G.M.Y., Shi B., Xue H.D. et al. Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma? Clin Radiol 2019;74(4):287-94. DOI:10.1016/j.crad.2018.11.009.; Duan C., Li N., Niu L. et al. CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes. Abdom Radiol 2020;45(11):3860-8. DOI:10.1007/s00261-020-02588-2.; Wang W., Cao K.M., Jin S.M. et al. Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Eur Radiol 2020;30(10):5738—47. DOI:10.1007/s00330-020-06896-5.; Fuhrman S., Lasky L.C., Limas C. Prognostic significance of morphologic parametrs in renal cell carcinoma. Am J Surg Pathol 1982;6(7):655-63. DOI:10.1097/00000478-198210000-00007.; Moch H., Cubilla A.L., Humphrey P.A. et al. The 2016 WHO classification of tumours of the urinary system and male genital organs — part A: renal, penile, and testicular tumours. Eur Urol 2016;70(1):93-105. DOI:10.1016/j.eururo.2016.02.029.; Tsui K.H., Shvarts O., Smith R.B. et al. Prognostic indicators for renal cell carcinoma: a multivariate analysis of 643 patients using the revised 1997 TNM staging criteria. J Urol 2000;163(4):1090-5. DOI:10.1016/S0022-5347(05)67699-9.; Feng Z., Shen Q., Li Y., Hu Z. CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma. Cancer Imaging 2019;19(1):6. DOI:10.1186/s40644-019-0195-7.; Goyal A., Razik A., Kandasamy D. et al. Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study. Abdom Radiol 2019;44(10):3336-49. DOI:10.1007/s00261-019-02122-z.; Boos J., Revah G., Brook O.R. et al. CT intensity distribution curve (Histogram) analysis of patients undergoing antiangiogenic therapy for metastatic renal cell carcinoma. Am J Roentgenol 2017;209(2):W85-92. DOI:10.2214/AJR.16.17651.; Bharwani N., Miquel M.E., Powles T. et al. Diffusion-weighted and multiphase contrast-enhanced MRI as surrogate markers of response to neoadjuvant sunitinib in metastatic renal cell carcinoma. Br J Cancer 2014;110(3):616-24. DOI:10.1038/bjc.2013.790.; Ueno D., Yao M., Tateishi U. et al. Early assessment by FDG-PET/CT of patients with advanced renal cell carcinoma treated with tyrosine kinase inhibitors is predictive of disease course. BMC Cancer 2012;12:162. DOI:10.1186/1471-2407-12-162.; Antunes J., Viswanath S., Rusu M. et al. Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol 2016;9(2):155-62. DOI:10.1016/j.tranon.2016.01.008.; Goh V., Ganeshan B., Nathan P. et al. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 2011;261(1):165-71. DOI:10.1148/radiol.11110264.; https://oncourology.abvpress.ru/oncur/article/view/1436
-
17Academic Journal
المؤلفون: I. S. Gruzdev, G. G. Karmazanovsky, M. G. Lapteva, K. A. Zamyatina, V. S. Tikhonova, E. V. Kondratyev, V. Yu. Struchkov, A. V. Glotov, I. S. Proskuryakov, D. V. Podluzhny, A. Sh. Revishvili, И. С. Груздев, Г. Г. Кармазановский, М. Г. Лаптева, К. А. Замятина, В. С. Тихонова, Е. В. Кондратьев, В. Ю. Стручков, А. В. Глотов, И. С. Проскуряков, Д. В. Подлужный, А. Ш. Ревишвили
المساهمون: The reported study was funded by RFBR, project number 20-315-90070., Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 20-315-90070.
المصدر: Medical Visualization; Том 26, № 4 (2022); 102-109 ; Медицинская визуализация; Том 26, № 4 (2022); 102-109 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: нейроэндокринная опухоль поджелудочной железы, texture analysis, renal cell carcinoma, metastases, pancreatic neuroendocrine tumor, текстурный анализ, почечно-клеточный рак, метастазы
وصف الملف: application/pdf
Relation: https://medvis.vidar.ru/jour/article/view/1279/765; Ouzaid I., Capitanio U., Staehler M. et al. Surgical Metastasectomy in Renal Cell Carcinoma: A Systematic Review. Eur. Urol. Oncol. 2019; 2 (2): 141–149. https://doi.org/10.1016/J.EUO.2018.08.028; Padala S.A., Barsouk A., Thandra K.C. et al. Epidemiology of Renal Cell Carcinoma. Wld J. Oncol. 2020; 11 (3): 79–87. https://doi.org/10.14740/WJON1279; Shah M.H., Goldner W.S., Benson A.B. et al. Neuroendocrine and Adrenal Tumors, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc. Netw. 2021; 19 (7): 839–867. https://doi.org/10.6004/JNCCN.2021.0032; Campbell S.C., Uzzo R.G., Karam J.A. et al. Renal Mass and Localized Renal Cancer: Evaluation, Management, and Follow-up: AUA Guideline: Part II. J. Urol. 2021; 206 (2): 209–218. https://doi.org/10.1097/JU.0000000000001912; Almeida R.R., Lo G.C., Patino M. et al. Advances in Pancreatic CT Imaging. Am. J. Roentgenol. 2018; 211 (1): 52–66. https://doi.org/10.2214/AJR.17.18665; Lee N.J., Hruban R.H., Fishman E.K. Pancreatic neuroendocrine tumor: review of heterogeneous spectrum of CT appearance. Abdom. Radiol. 2018; 43 (11): 3025–3034. https://doi.org/10.1007/s00261-018-1574-4; Sellner F. Observations on Solitary Versus Multiple Isolated Pancreatic Metastases of Renal Cell Carcinoma: Another Indication of a Seed and Soil Mechanism? Cancers. 2019; 11 (9): 1379. https://doi.org/10.3390/CANCERS11091379; Nogueira M., Dias S.C., Silva A.C. et al. Solitary pancreatic renal cell carcinoma metastasis. Autopsy. Case Reports. 2018; 8 (2): e2018023. https://doi.org/10.4322/ACR.2018.023; Akirov A., Larouche V., Alshehri S. et al. Treatment Options for Pancreatic Neuroendocrine Tumors. Cancers. 2019; 11 (6): 828. https://doi.org/10.3390/CANCERS11060828; Barthet M., Giovannini M., Lesavre N. et al. Endoscopic ultrasound-guided radiofrequency ablation for pancreatic neuroendocrine tumors and pancreatic cystic neoplasms: A prospective multicenter study. Endoscopy. 2019; 51 (9): 836–842. https://doi.org/10.1055/A-0824-7067/ID/JR17031-18; Fazio N., Kulke M., Rosbrook B. et al. Updated Efficacy and Safety Outcomes for Patients with Well-Differentiated Pancreatic Neuroendocrine Tumors Treated with Sunitinib. Target. Oncol. 2021; 16 (1): 27–35. https://doi.org/10.1007/S11523-020-00784-0/FIGURES/4; Quhal F., Mori K., Bruchbacher A. et al. First-line Immunotherapy-based Combinations for Metastatic Renal Cell Carcinoma: A Systematic Review and Network Meta-analysis. Eur. Urol. Oncol. 2021; 4 (5): 755–765. https://doi.org/10.1016/J.EUO.2021.03.001; Powles T., Albiges L., Bex A. et al. ESMO Clinical Practice Guideline update on the use of immunotherapy in early stage and advanced renal cell carcinoma. Ann. Oncol. 2021; 32 (12): 1511–1519. https://doi.org/10.1016/J.ANNONC.2021.09.014; Gu D., Hu Y., Ding H. et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur. Radiol. 2019; 29 (12): 6880–6890. https://doi.org/10.1007/s00330-019-06176-x; Lin X., Xu L., Wu A. et al. Differentiation of intrapancreatic accessory spleen from small hypervascular neuroendocrine tumor of the pancreas: textural analysis on contrast-enhanced computed tomography. Acta Radiol. 60 (2019) 553–560. https://doi.org/10.1177/0284185118788895; Karmazanovsky G., Gruzdev I., Tikhonova V. et al. Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol. Medica. 2021; 126: 1388–1395. https://doi.org/10.1007/S11547-021-01405-0/FIGURES/1; van der Pol C.B., Lee S., Tsai S. et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdom. Radiol. 2019; 44(3): 992–999. https://doi.org/10.1007/s00261-018-01889-x; Nioche C., Orlhac F., Boughdad S. et al. Lifex: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018; 78 (16): 4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125; Gruzdev I.S., Zamyatina K.A., Tikhonova V.S. et al. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur. J. Radiol. 2020; 133: 109371. https://doi.org/10.1016/j.ejrad.2020.109371; Kang T.W., Kim S.H., Lee J. et al. Differentiation between pancreatic metastases from renal cell carcinoma and hypervascular neuroendocrine tumour: Use of relative percentage washout value and its clinical implication. Eur. J. Radiol. 2015; 84 (11): 2089–2096. https://doi.org/10.1016/J.EJRAD.2015.08.007; Lyu H.-L., Cao J.-X., Wang H.-Y. et al. Differentiation between pancreatic metastases from clear cell renal cell carcinoma and pancreatic neuroendocrine tumor using double-echo chemical shift imaging. Abdom. Radiol. 2018; 43 (10): 2712–2720. https://doi.org/10.1007/s00261-018-1539-7; Ambrosetti M.C., Zamboni G.A., Fighera A., Mansueto G. Pancreatic metastases from renal neoplasms and neuroendocrine pancreatic tumours: is a differential diagnosis possible with CT? Hell. J. of Radiol. 2019; 4 (3). 17–21. https://doi.org/10.36162/HJR.V4I3.295; https://medvis.vidar.ru/jour/article/view/1279
-
18Academic Journal
المؤلفون: V. Tikhonova S., I. Gruzdev S., E. Kondratyev V., K. Mikhaylyuk A., G. Kаrmаzаnovsky G., В. Тихонова С., И. Груздев С., Е. Кондратьев В., К. Михайлюк А., Г. Кармазановский Г.
المساهمون: The reported study was funded by Russian Foundation for Basic Research according to the research project № 20-315-90092. The funding source had not any involvement in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication., Настоящее исследование финансировалось Российским фондом фундаментальных исследований по исследовательскому проекту № 20-315-90092. Источник финансирования не участвовал в разработке исследования, в сборе, анализе и интерпретации данных, при написании отчета и в решении о подаче статьи для публикации.
المصدر: Medical Visualization; Том 26, № 1 (2022); 140-154 ; Медицинская визуализация; Том 26, № 1 (2022); 140-154 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: ductal adenocarcinoma of the pancreas, radiomics features, computed tomography, mass-forming pancreatitis, протоковая аденокарцинома поджелудочной железы, текстурный анализ, компьютерная томография, псевдотуморозный панкреатит
وصف الملف: application/pdf
Relation: https://medvis.vidar.ru/jour/article/view/1068/718; https://medvis.vidar.ru/jour/article/downloadSuppFile/1068/1406; Sung H., Ferlay J., Siegel R.L. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021; 71 (3): 209–249. https://doi.org/10.3322/caac.21660; Sarantis P., Koustas E., Papadimitropoulou A. et al. Pancreatic ductal adenocarcinoma: Treatment hurdles, tumor microenvironment and immunotherapy. Wld J. Gastrointest. Oncol. 2020; 12 (2): 173–181. https://doi.org/10.4251/wjgo.v12.i2.173; Wolske K.M., Ponnatapura J., Kolokythas O. et al. Chronic Pancreatitis or Pancreatic Tumor? A Problem-solving Approach. Radiographics. 2019; 39 (7): 1965–1982. https://doi.org/10.1148/rg.2019190011; Narkhede R.A., Desai G.S., Prasad P.P., Wagle P.K. Diagnosis and Management of Pancreatic Adenocarcinoma in the Background of Chronic Pancreatitis: Core Issues. Dig. Dis. 2019; 37 (4): 315–324. https://doi.org/10.1159/000496507; Meng F., Guo Y., Li M. et al. Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules. Transl. Oncol. 2021; 14 (1): 100936. https://doi.org/10.1016/j.tranon.2020.100936; Gillies R.J., Kinahan P.E., Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016; 278 (2): 563–577. https://doi.org/10.1148/radiol.2015151169; Park S., Chu L.C., Hruban R.H. et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn. Interv. Imaging. 2020; 101 (9): 555–564. https://doi.org/10.1016/j.diii.2020.03.002; Zhang Y., Cheng C., Liu Z. et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT. Med. Phys. 2019; 46: 4520–4530.; Zhang Y., Cheng C., Liu Z. et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT. Med. Phys. 2019; 46 (10): 4520–4530. https://doi.org/10.1002/mp.13733; Dai W., Mo S., Han L. et al. Prognostic and predictive value of radiomics signatures in stage I-III colon cancer. Clin Transl Med. 2020; 10 (1): 288–293. https://doi.org/10.1002/ctm2.31; Ren S., Zhao R., Zhang J. et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate massforming pancreatitis from pancreatic ductal adenocarcinoma. Abdominal. Radiol. (NY). 2020; 45 (5): 1524– 1533. https://doi.org/10.1007/s00261-020-02506-6; Ren S., Zhang J., Chen J. et al. Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images. Front. Oncol. 2019; 9: 1171. Published 2019 Nov 5. https://doi.org/10.3389/fonc.2019.01171; Washington M.K., Berlin J., Branton P.A. et al. Protocol for the examination of specimens from patients with carcinoma of the distal extrahepatic bile ducts. Arch. Pathol. Lab. Med. 2010; 134 (4): e8–e13. https://doi.org/10.5858/134.4.e8; Nioche C., Orlhac F., Boughdad S. et al. LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer Res. 2018; 78 (16): 4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125; Тихонова В.С., Кармазановский Г.Г., Кондратьев Е.В., Груздев И.С., Глотов А.В. Влияние параметров низкодозового протокола сканирования на результаты текстурного анализа протоковой аденокарциномы поджелудочной железы. Анналы хирургической гепатологии. 2021; 26 (1): 25–33. https://doi.org/10.16931/1995-5464.2021125-33. Tikhonova V.S., Karmazanovsky G.G., Kondratyev E.V., Gruzdev I.S., Glotov A.V. Influence of the low-dose CEMDCT scanning protocol parameters on the results of pancreatic ductal adenocarcinoma radiomic analysis. Annaly khirurgicheskoy gepatologii = Annals of HPB surgery. 2021; 26 (1): 25–33. https://doi.org/10.16931/1995-5464.2021125-33 (In Russian); Steyerberg E.W. Coding of categorical and continuous predictors. Clinical prediction models. Springer, Cham, 2019. 175–190. https://doi.org/10.1007/978-3-030-16399-0_9; Gareth J., Daniela W., Trevor H., Robert T. An introduction to statistical learning: with applications in R. Spinger, 2013. ISBN: 978-1-4614-7138-7. https://doi.org/10.18637/jss.v070.b02; Harrell F.E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. 2015 ed. Cham. Heidelberg; New York: Springer, 2015: 209–212.; Siddiqi A.J., Miller F. Chronic pancreatitis: ultrasound, computed tomography, and magnetic resonance imaging features. Semin. Ultrasound CT MR. 2007; 28 (5): 384–394. https://doi.org/10.1053/j.sult.2007.06.003; Sahani D.V., Kalva S.P., Farrell J. et al. Autoimmune pancreatitis: imaging features. Radiology. 2004; 233 (2): 345–352. https://doi.org/10.1148/radiol.2332031436; Ren S., Chen X., Cui W. et al. Differentiation of chronic mass-forming pancreatitis from pancreatic ductal adenocarcinoma using contrast-enhanced computed tomography. Cancer Managm. Res. 2019; 11: 7857–7866. https://doi.org/10.2147/CMAR.S217033; https://medvis.vidar.ru/jour/article/view/1068
-
19Academic Journal
المؤلفون: Крыштахович, Г. М.
المساهمون: Юржиц, С. Л., науч. рук.
مصطلحات موضوعية: color models, image analysis, multimedia systems, music generation, neural networks, texture analysis, аrtificial intelligence, анализ изображений, генерация музыки, искусственный интеллект, мультимедийные системы, нейронные сети, текстурный анализ, цветовые модели
وصف الملف: application/pdf
Relation: e0715783effb12854da5853fbe05162d; https://rep.vsu.by/handle/123456789/44680
-
20Academic Journal
المؤلفون: E. Popov V., Zh. Anashbaev Zh., A. Maltseva N., S. Sazonova I., Е. Попов В., Ж. Анашбаев Ж., А. Мальцева Н., С. Сазонова И.
المصدر: Complex Issues of Cardiovascular Diseases; Том 10, № 4 (2021); 6-16 ; Комплексные проблемы сердечно-сосудистых заболеваний; Том 10, № 4 (2021); 6-16 ; 2587-9537 ; 2306-1278
مصطلحات موضوعية: coronary atherosclerosis, cardiac ischemia, radiomics, nexture analysi, атеросклероз коронарных артерий, ишемическая болезнь сердца, радиомика, текстурный анализ
وصف الملف: application/pdf
Relation: https://www.nii-kpssz.com/jour/article/view/993/610; Демографический ежегодник России. Статистический сборник. Росстат. Москва; 2019. 252 с Режим доступа: https://rosstat.gov.ru/storage/mediabank/Dem_ejegod-2019.pdf (rosstat.gov.ru) (дата обращения 15.11.2021); Townsend N., Wilson L., Bhatnagar P., Wickramasinghe K., Rayner M., Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016;37(42):3232- 3245. doi:10.1093/eurheartj/ehw334.; Dey D., Wong N.D., Tamarappoo B., Nakazato R., Gransar H., Cheng V.Y., Ramesh A., Kakadiaris I., Germano G., Slomka P.J., Berman D.S. Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome. Atherosclerosis. 2010;209(1):136-41. doi:10.1016/j.atherosclerosis.2009.08.032.; Berg A.H., Scherer P.E. Adipose tissue, inflammation, and cardiovascular disease. Circ Res. 2005; 96(9):939–949. doi:10.1161/01.RES.0000163635.62927.34; Alexopoulos N., McLean D.S., Janik M., Arepalli C.D., Stillman A.E., Raggi P. Epicardial adipose tissue and coronary artery plaque characteristics. Atherosclerosis. 2010; 210(1):150- 4. doi:10.1016/j.atherosclerosis.2009.11.020.; Khawaja T., Greer C., Thadani S.R., Kato T.S., Bhatia K., Shimbo D., Kontak A., Bokhari S., Einstein A.J., Schulze P.C. Increased Regional Epicardial Fat Volume Associated with Reversible Myocardial Ischemia in Patients with Suspected Coronary Artery Disease. Journal of Nuclear Cardiology. 2015; 22(2): 325–333. doi:10.1007/s12350-014-0004-4; Ohashi N., Yamamoto H., Horiguchi J., Kitagawa T., Kunita E., Utsunomiya H., Oka T., Kohno N., Kihara Y. Association between visceral adipose tissue area and coronary plaque morphology assessed by CT angiography. JACC Cardiovasc Imaging. 2010; 3(9):908-17. doi: :10.1016/j.jcmg.2010.06.014; Shaikh F., Franc B., Mulero F. Radiomics as Applied in Precision Medicine. In: Clinical Nuclear Medicine. Ahmadzadehfar H., Biersack H.J., Freeman L.M., Zuckier L.S. editors. 2nd ed. Springer-Verlag Berlin Heidelberg; 2020. 193-206.; Завадовский К.В., Гуля М.О., Саушкин В.В., Саушкина Ю.В., Лишманов Ю.Б. Cовмещенная однофотонная эмиссионная и рентгеновская компьютерная томография сердца: методические аспекты. 2016; 97(4):235-242. doi:10.20862/0042-4676-2016-97-4-8-15; Neumann F.-J., Sousa-Uva M., Ahlsson A., Alfonso F., Banning A. P., Benedetto U. 2018 ESC/EACTS guidelines on myocardial revascularization. The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Associationfor Cardio-Thoracic Surgery (EACTS). Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI). European Heart Journal. 2018; 40(37): 87-165. doi:10.1093/eurheartj/ehy394; Ficaro E., Lee B., Kritzman J., Corbett J. The Michigan method for quantitative nuclear cardiology. Corridor4DM: The Michigan method for quantitative nuclear cardiology. Journal of Nuclear Cardiology. 2007; 14(4):455-65. doi:10.1016/j.nuclcard.2007.06.006; Prasad M., Slomka P.J., Fish M., Kavanagh P., Gerlach J., Hayes S., Berman D. S., Germano G. Improved quantification and normal limits for myocardial perfusion stress-rest change. Journal of Nuclear Medicine. 2010; 51(2): 204-9. doi:10.2967/jnumed.109.067736; Cerqueira M.D., Weissman N. J., Dilsizian V., Jacobs A. K., Kaul S., Laskey W. K., Pennell D.J., Rumberger J.A., Ryan T., Verani M.S.; American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association. Circulation. 2002; 105: 539-542. doi:10.1161/hc0402.102975; Oikonomou E.K., Williams M.C., Kotanidis C.P., Desai M.Y., Marwan M., Antonopoulos A.S., et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CTangiography. European Heart Journal. 2019; 40(43):3529-3543. doi:10.1093/eurheartj/ehz592; Kolossváry M., Karady J., Szilveszter B., Kitslaar P., Hoffmann U., Merkely B., Maurovich-Horvat P. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with NapkinRing Sign. Circ Cardiovasc Imaging. 2017;10(12), e006843. doi:10.1161/CIRCIMAGING.117.006843; Kolossváry M., Kellermayer M., Merkely B., Maurovich-Horvat P., Maurovich-Horvat P. Cardiac computed tomography radiomics: a comprehensive review on radiomic techniques. J Thorac Imaging. 2018; 33(1):26–34. doi:10.1097/RTI.0000000000000268; Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., van Stiphout R.G., Granton P., Zegers C.M., Gillies R., Boellard R., Dekker A., Aerts H.J. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012; 48(4):441-6. doi:10.1016/j.ejca.2011.11.036; De Jong M.C., Genders T.S.S., Van Geuns R-J., Moelker A., Hunink M.G.M. Diagnostic performance of stress myocardial perfusion imaging for coronary artery disease: a systematic review and meta-analysis. European Radiology. 2012; 22 (9): 1881–1895. doi:10.1007/s00330-012-2434-1; Radiomic Features. Available at: https://pyradiomics.readthedocs.io/en/latest/features.html. (accessed: 01.07.2020); Knuuti J., Wijns W., Saraste A., Capodanno D., Barbato E., Funck-Brentano C., Prescott E., Storey R.F., Deaton C., Cuisset T., Agewall S., Dickstein K., Edvardsen T., Escaned J., Gersh B.J., Svitil P., Gilard M., Hasdai D., Hatala R., Mahfoud F., Masip J., Muneretto C., Valgimigli M., Achenbach S., Bax J.J.; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). European Heart Journal. 2019;41: 407-477.doi:10.1093/eurheartj/ehz425; Agatston A.S., Janowitz W.R., Hildner F.J., Zusmer N.R., Viamonte M., Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. Journal of the American College of Cardiology. 1990; 15(4): 827-832. doi:10.1016/0735-1097(90)90282-T; Hyafil F., Gimelli A., Slart R.H.J.A., Georgoulias P., Rischpler C., Lubberink M., Sciagra R., Bucerius J., Agostini D., Verberne H.J., behalf of the Cardiovascular Committee of the European Association of Nuclear Medicine (EANM). EANM procedural guidelines for myocardial perfusion scintigraphy using cardiac-centered gamma cameras. European J Hybrid Imaging. 2019; 3(11): doi.org/10.1186/s41824-019-0058-2; A.N Kokov, N.K. Brel, V.L. Masenko, O.V. Gruzdeva, V.N. Karetnikova, V.V. Kashtalap, O.L. Barbarash. Quanntitative assessment of visceral adipose depot in patients with ischemic heart disease by using of modern tomographic methods. Complex Issues of Cardiovascular Diseases. 2017;3:113-119. doi:10.17802/2306-1278-2017-6-3-113-119. (In Russian); Mazurek T., Zhang L., Zalewski A., Mannion J.D., Diehl J.T., Arafat H., Sarov-Blat L., O'Brien S., Keiper E.A., Johnson A.G., Martin J., Goldstein B.J., Shi Y. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation. 2003; 108(20):2460-6. doi:10.1161/01.CIR.0000099542.57313.C5; Kolossváry M., Park J., Bang J.I., Zhang J., Lee J.M., Paeng J.C., Merkely B., Narula J., Kubo T., Akasaka T., Koo B.K., Maurovich-Horvat P. Identification of invasive and radionuclide imagingmarkers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. European Heart Journal - Cardiovascular Imaging. 2019; 20(11): 1250–1258. doi:10.1093/ehjci/jez033; https://www.nii-kpssz.com/jour/article/view/993