Explainable Fusion Model for Predicting Postoperative Early Recurrence in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI Habitat Imaging.

Rationale and objectives: To develop an explainable fusion model that combines clinical, radiomic, and habitat features to predict postoperative early recurrence in hepatocellular carcinoma (HCC).

Methods: The bicentric retrospective study included 370 patients with surgically confirmed early-stage HCC who underwent gadoxetic acid-enhanced MRI. The patients were stratified into a training cohort (n=296) and an external validation cohort (n=74). From the hepatobiliary phase images, habitat and radiomics features were extracted across the entire tumor and used to construct radiomics and habitat models. Additionally, a clinical model was established utilizing relevant clinical features. Subsequently, all previously mentioned features were merged to construct the fusion model (HabRad_FB). Diagnostic performance of these models was assessed and compared using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). The fusion model was then interpreted using SHapley Additive exPlanations (SHAP) analysis.

Results: Tumor recurrence was observed in 73 out of 370 patients (19.7%; 55.2±11.3 years; male=333). Among all study cohorts, the HabRad_FB model showed the highest AUC (0.820-0.959), outperforming the clinical (0.517-0.729), radiomics (0.707-0.815), and habitat (0.729-0.861) models. The HabRad_FB model also demonstrated significant improvement in IDI in the training cohort and NRI in the validation cohort. SHAP force plots provided valuable insights into the interpretation of HabRad_FB model's predictions for early recurrence.

Conclusion: The HabRad_FB, an explainable fusion model, aids clinicians in accurately and non-invasively predicting the early recurrence of HCC preoperatively. This model might provide great potential in prognostic prediction and clinical management.