AI Multimodal Model for Liver Cancer Diagnosis and Prognosis

N/AActive Not RecruitingNCT07658586

Guangxi Medical University600 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

600

Conditions

Liver Cancer Hepatocellular Carcinoma Intrahepatic Cholangiocarcinoma (Icc)

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification

The diagnostic performance of the multimodal AI model in differentiating benign from malignant liver lesions and distinguishing hepatocellular carcinoma from intrahepatic cholangiocarcinoma, evaluated using pathology results as the gold standard. Performance metrics include area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Time frame: At the time of initial diagnosis

Prognostic Performance of the Multimodal AI Model for Postoperative Survival Prediction

The prognostic performance of the multimodal AI model in predicting postoperative progression-free survival (PFS) and overall survival (OS) in patients with pathologically confirmed liver cancer who underwent radical hepatectomy. Performance metric includes the concordance index (C-index). Calibration curves are also assessed.

Time frame: minimum follow-up of 24 months