Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2026.1767885

Authors: Zhe Wang, Xi-Ran Li, Zhong-Yi Huang, Jia-He Chu, Kun Liu, Qian Ji

Institutions: Tianjin First Center Hospital, Tianjin Medical University, Hebei University, Nankai University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801