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AI Multimodal Model for Liver Cancer Diagnosis and Prognosis
Sponsor: Guangxi Medical University
Summary
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.
Official title: A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data
Key Details
Gender
All
Age Range
18 Years - 80 Years
Study Type
OBSERVATIONAL
Enrollment
600
Start Date
2025-12-01
Completion Date
2028-12-01
Last Updated
2026-07-01
Healthy Volunteers
No
Locations (1)
Guangxi Medical University First Affiliated Hospital
Nanning, Guangxi, China