Habitat imaging on contrast-enhanced magnetic resonance imaging predicts early response to transarterial chemoembolization in hepatocellular carcinoma

BACKGROUND Transcatheter arterial chemoembolization (TACE) is a core treatment for hepatocellular carcinoma (HCC), but significant variability in therapeutic responses exists due to intratumoral heterogeneity (ITH). Conventional predictive methods ignore subregional tumor heterogeneity, limiting their accuracy and generalizability. Habitat imaging enables non-invasive quantification of ITH by characterizing tumor subregional diversity, yet few studies have integrated this technique with clinical and radiomic features to enhance TACE response prediction. AIM To develop and validate a combined model integrating clinical, radiomics, and ITH features for predicting early response to TACE in HCC patients. METHODS A total of 223 HCC patients were divided into training (n = 107), internal validation (n = 46), and external validation (n = 70) cohorts. Preoperative magnetic resonance imaging images were processed via simple linear iterative clustering superpixel segmentation and Gaussian mixture model clustering (Bayesian information criterion-optimized) to quantify tumor ecological diversity. The ITH and radiomic features were extracted and selected by recursive feature elimination with cross-validation. A combined model was constructed and evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. We employed SHapley Additive exPlanations analysis to quantify the contributions of each feature to the combined model. RESULTS The combined model achieved the highest area under the curve (AUC) of 0.97 (95% confidence interval: 0.93-0.99) in the training set, outperforming clinical (AUC = 0.86) and radiomics (AUC = 0.71) models. It maintained robust performance in internal (AUC = 0.91) and external (AUC = 0.93) validation cohorts, with good calibration and superior clinical net benefit in decision curve analysis. CONCLUSION Integrating ITH features significantly enhances the prediction of TACE response in HCC. The combined model exhibits excellent discriminative power and generalizability, fulfilling the objective of providing a reliable tool for personalized treatment decision-making.