Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma.

backgroundTransarterial chemoembolization (TACE) is important role for the treatment of unresectable hepatocellular carcinoma (HCC). However, owing to the heterogeneity of HCC tumors, TACE efficacy varies among individual HCC patients. Accurate preoperative prediction of responses to TACE among HCC patients could guide the development of individualized treatment strategies and improve patient outcomes.

aimTo investigate the predictive value of multiple-sequence magnetic resonance imaging (MRI) radiomic features combined with clinical indices for the response to TACE among HCC patients and to develop an interpretable machine learning model.

methodsA total of 116 patients with HCC who underwent TACE were retrospectively enrolled. Patients in this study were randomly divided into a training group and a validation group at a ratio of 7:3. The response to TACE was evaluated according to the modified Response Evaluation Criteria in Solid Tumors. Radiomic features were extracted from axial fat-suppressed T2-weighted imaging, arterial phase and portal venous phase axial dynamic contrast-enhanced MRI sequences. The least absolute shrinkage and selection operator method was used to select the best radiomic features. Univariate and multivariate logistic regression were used to select clinical predictive factors that affect the response to TACE among patients with HCC. Logistic regression was used to construct a radiomic model of each sequence, a multiple-sequence MRI radiomics model, and a radiomic-clinical (RC) model. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve. The risk factors in the RC model were subsequently interpreted

resultsThe area under the receiver operating characteristic curve values of the fat-suppressed T2-weighted imaging, arterial-phase, portal-venous-phase, joint-radiomic model and RC model were 0.771, 0.668, 0.725, 0.845 and 0.929, respectively, in the training group and 0.705, 0.666, 0.675, 0.799 and 0.815, respectively, in the validation group. The clinical-radiomic model had the best predictive performance. The SHapley Additive exPlanations algorithm was used to illustrate the contribution of each feature in the RC model.

conclusionThe interpretable RC model could successfully stratify HCC patients into TACE responders and TACE nonresponders and serve as a potential tool to identify more appropriate HCC patients for TACE, thus sparing patients from receiving ineffective and unnecessary treatments.