AI Multimodal Model for Liver Cancer Diagnosis and Prognosis

A Comprehensive Study of Liver Cancer Diagnosis and Prognosis Prediction Based on Artificial Intelligence and Multimodal Data

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.

Inclusion Criteria:

-Diagnostic Model Cohort:

• Age ≥18 years
• Underwent preoperative contrast-enhanced CT or MRI for clinically suspected liver space-occupying lesion
• Have complete preoperative clinical laboratory data
• Have complete original CT/MRI imaging data and radiology reports
• Have definite pathological diagnosis from surgery or biopsy as gold standard

Prognostic Prediction Model Cohort (selected from diagnostic cohort):

• Meet all diagnostic cohort inclusion criteria
• Pathologically confirmed liver cancer
• Underwent radical hepatectomy
• Have complete preoperative multimodal data (CT/MRI imaging, clinical laboratory data, radiology reports)
• Have complete postoperative follow-up data to determine progression-free survival and overall survival endpoints and time (minimum follow-up of 24 months)

Exclusion Criteria:

• · Key clinical, imaging, or pathological data severely missing or incomplete

  • Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis
  • Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery
  • Concurrent other malignant tumors
  • Lost to follow-up or follow-up data cannot meet endpoint determination requirements