Multiphasic CT-based multimodal deep learning model for predicting early hepatocellular carcinoma recurrence following liver transplantation

Objective This study aimed to develop a multimodal deep learning (MD DL) model integrating multiphasic computed tomography (CT) with clinical and laboratory parameters to predict early recurrence of hepatocellular carcinoma (HCC) following liver transplantation. Methods A retrospective analysis was conducted on 147 patients with HCC who underwent liver transplantation at Tianjin First Central Hospital between June 2014 and September 2022. Patients were categorized into recurrence ( n = 40) and non-recurrence ( n = 107) groups. Independent risk factors for early recurrence were identified to construct a clinical-imaging model. Deep learning models were developed using both single-phase and multiphasic CT images. High-dimensional imaging features were combined with clinicoradiological parameters to establish the MD DL model. Model performance was evaluated using receiver operating characteristic curves and the DeLong test, while interpretability was assessed through SHapley Additive explanation (SHAP) analysis. Results Independent risk factors for early recurrence included platelet count, alpha-fetoprotein levels > 400 ng/mL, ascites, arterial peritumoral enhancement, and portal vein tumor thrombus. The MD DL model achieved area under the curve values of 0.972, 0.885, and 0.985 in the training, validation, and test sets, respectively. These values indicated significantly superior predictive performance compared with other models (all p < 0.05). SHAP analysis identified key predictive features contributing to model performance. Conclusion The MD DL model integrating multiphasic CT and clinical parameters demonstrated high predictive accuracy for early recurrence of HCC after liver transplantation, with diagnostic performance exceeding that of conventional models.