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: 1.5T or 3.0T, gradient-recalled-echo in-phase T1 -weighted imaging (I-T1 WI) and opposed-phase T1 WI (O-T1 WI), fast spin-echo T2 -weighted imaging (T2 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: The radiomics model based on I-T1 WI, O-T1 WI, T2 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 nomogram (AUC: 0.873; 95% CI: 0.756-0.989) outperformed the radiomics model and the CPR model (AUC: 0.742; 95% CI: 0.577-0.907). DCA demonstrated that the combined nomogram was clinically useful. DATA CONCLUSION: The mpMRI-based radiomics analysis has potential to predict ER of HCC patients after hepatectomy, which could enhance risk stratification and provide support for individualized treatment planning. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 4.
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: 1.5T or 3.0T, gradient-recalled-echo in-phase T1 -weighted imaging (I-T1 WI) and opposed-phase T1 WI (O-T1 WI), fast spin-echo T2 -weighted imaging (T2 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: The radiomics model based on I-T1 WI, O-T1 WI, T2 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 nomogram (AUC: 0.873; 95% CI: 0.756-0.989) outperformed the radiomics model and the CPR model (AUC: 0.742; 95% CI: 0.577-0.907). DCA demonstrated that the combined nomogram was clinically useful. DATA CONCLUSION: The mpMRI-based radiomics analysis has potential to predict ER of HCC patients after hepatectomy, which could enhance risk stratification and provide support for individualized treatment planning. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 4.