Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation
| العنوان: | Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation |
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| المؤلفون: | Tingfan Wu, Qiao-feng Chen, Shi-Ting Feng, Qian Zhou, Zhihang Chen, Xin Li, Jingxian Shen, Shuling Chen, Sui Peng, Ming Kuang |
| المصدر: | Translational Oncology, Vol 14, Iss 1, Pp 100866-(2021) Translational Oncology |
| بيانات النشر: | Elsevier, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0301 basic medicine, Cancer Research, medicine.medical_treatment, DD, difference-in-difference, ROI, region of interest, lcsh:RC254-282, Cross-validation, Resection, 03 medical and health sciences, AUC, area under the receiver operating characteristic curve, 0302 clinical medicine, Text mining, GLRLM, gray level run-length matrix, Radiomics, Medicine, Original Research, ICC, intraclass correlation coefficient, AFP, alpha-fetoprotein, Receiver operating characteristic, business.industry, medicine.disease, Ablation, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Random forest, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, Hepatocellular carcinoma, CV, cross validation, ROC, receiver operating characteristic curve, business, HCC, hepatocellular carcinoma, Algorithm, GLCM, gray level co-occurrence matrix |
| الوصف: | Objectives To develop a radiomics algorithm, improving the performance of detecting recurrence, based on posttreatment CT images within one month and at suspicious time during follow-up. Materials and methods A total of 114 patients with 228 images were randomly split (7:3) into training and validation cohort. Radiomics algorithm was trained using machine learning, based on difference-in-difference (DD) features extracted from tumor and liver regions of interest on posttreatment CTs within one month after resection or ablation and when suspected recurrent lesion was observed but cannot be confirmed as HCC during follow-up. The performance was evaluated by area under the receiver operating characteristic curve (AUC) and was compared among radiomics algorithm, change of alpha-fetoprotein (AFP) and combined model of both. Five-folded cross validation (CV) was used to present the training error. Results A radiomics algorithm was established by 34 DD features selected by random forest and multivariable logistic models and showed a better AUC than that of change of AFP (0.89 [95% CI: 0.78, 1.00] vs 0.63 [95% CI: 0.42, 0.84], P = .04) and similar with the combined model in detecting recurrence in the validation set. Five-folded CV error in the validation cohort was 21% for the algorithm and 26% for the changes of AFP. Conclusions The algorithm integrated radiomic features of posttreatment CT showed superior performance to that of conventional AFP and may act as a potential marker in the early detecting recurrence of HCC. |
| اللغة: | English |
| تدمد: | 1936-5233 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb9e293a812221a3cf091e8dd1504813 http://www.sciencedirect.com/science/article/pii/S1936523320303582 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....cb9e293a812221a3cf091e8dd1504813 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19365233 |
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