Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy

作者：Ying Zhao, Jingjun Wu, Qinhe Zhang, Zhengyu Hua, Wenjing Qi, Nan Wang, Tao Lin, Liuji Sheng, Dahua Cui, Jinghong Liu, Qingwei Song, Xin Li, Tingfan Wu, Yan Guo, Jingjing Cui, Ailian Liu · 发表于：Journal of Magnetic Resonance Imaging · 年份：2020 · DOI：10.1002/jmri.27424 · 被引用次数：71 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Cholangiocarcinoma and Gallbladder Cancer Studies

BACKGROUND: Preoperative prediction of early recurrence (ER) of hepatocellular carcinoma (HCC) plays a critical role in individualized risk stratification and further treatment guidance. PURPOSE: To investigate the role of radiomics analysis based on multiparametric MRI (mpMRI) for predicting ER in HCC after partial hepatectomy. STUDY TYPE: Retrospective. POPULATION: In all, 113 HCC patients (ER, n = 58 vs. non-ER, n = 55), divided into training (n = 78) and validation (n = 35) cohorts. FIELD STRENGTH/SEQUENCE: WI), spin-echo planar diffusion-weighted imaging (DWI), and gradient-recalled-echo contrast-enhanced MRI (CE-MRI). ASSESSMENT: In all, 1146 radiomics features were extracted from each image sequence, and radiomics models based on each sequence and their combination were established via multivariate logistic regression analysis. The clinicopathologic-radiologic (CPR) model and the combined model integrating the radiomics score with the CPR risk factors were constructed. A nomogram based on the combined model was established. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of each model. The potential clinical usefulness was evaluated by decision curve analysis (DCA). RESULTS: WI, and CE-MRI sequences presented the best performance among all radiomics models with an area under the ROC curve (AUC) of 0.771 (95% confidence interval (CI): 0.598-0.894) in the validation cohort. The combined nomogra...