AI Multimodal Model for Liver Cancer Diagnosis and Prognosis

This study aims to develop a comprehensive artificial intelligence model system integrating preoperative multimodal data (CT/MRI imaging, clinical laboratory data, and radiology report text) to achieve two core objectives. First, to develop a multimodal fusion diagnostic model for non-invasive and accurate preoperative differentiation of liver cancer subtypes, including distinguishing benign from malignant lesions and differentiating hepatocellular carcinoma from intrahepatic cholangiocarcinoma. Second, to develop a prognostic prediction model for patients with confirmed liver cancer undergoing radical surgery to assess postoperative progression-free survival and overall survival. This is a multicenter retrospective cohort study with an anticipated sample size of ≥600 patients. Model performance will be evaluated using AUC, accuracy, sensitivity, specificity, C-index, and calibration curves. Subgroup analysis will be conducted based on whether patients received neoadjuvant therapy.

NCT Number	NCT07658586
Lead Sponsor	Guangxi Medical University
Conditions	Liver Cancer, Hepatocellular Carcinoma, Intrahepatic Cholangiocarcinoma (Icc)
Enrollment	600 participants
Start Date	2025-12-01
Primary Completion	2028-12-01 (estimated)
Study Completion	2028-12-01 (estimated)
Updated on ClinicalTrials.gov	2026-07-01