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1Academic 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
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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
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2Academic 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. 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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. 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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. 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3Academic Journal
المؤلفون: G. G. Kаrmаzаnovsky, K. A. Zamyatina, V. I. Stashkiv, M. Yu. Shantarevich, E. V. Kondratyev, F. M. Semenov, S. Yu. Kuznetsova, A. V. Kozlova, G. P. Plotnikov, V. A. Popov, A. V. Chupin, A. A. Gritskevich, A. M. Chililov, A. A. Pechetov, A. I. Kurochkina, V. A. Khokhlov, D. V. Kalinin, Г. Г. Кармазановский, К. А. Замятина, В. И. Сташкив, М. Ю. Шантаревич, Е. В. Кондратьев, Ф. М. Семенов, С. Ю. Кузнецова, А. В. Козлова, Г. П. Плотников, В. А. Попов, А. В. Чупин, А. А. Грицкевич, А. М. Чилилов, А. А. Печетов, А. И. Курочкина, В. А. Хохлов, Д. В. Калинин
المصدر: Medical Visualization; Том 24, № 2 (2020); 11-36 ; Медицинская визуализация; Том 24, № 2 (2020); 11-36 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: консолидация, CT, viral pneumonia, ground-glass opacity, consolidation, КТ, вирусная пневмония, “матовое стекло”
وصف الملف: application/pdf
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4Academic Journal
المؤلفون: V. I. Stashkiv, K. A. Zamyatina, M. Yu. Shantarevich, I. V. Nikitina, G. G. Kаrmаzаnovsky, A. Sh. Revishvili, В. И. Сташкив, К. А. Замятина, М. Ю. Шантаревич, И. В. Никитина, Г. Г. Кармазановский, А. Ш. Ревишвили
المصدر: Medical Visualization; Том 24, № 2 (2020); 96-104 ; Медицинская визуализация; Том 24, № 2 (2020); 96-104 ; 2408-9516 ; 1607-0763
مصطلحات موضوعية: ИЛ-6, CT, tocilizumab, IL-6, тоцилизумаб, КТ
وصف الملف: application/pdf
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