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.

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Key information

Conditions

Liver Cancer Adenocarcinoma Carcinoma Carcinoma, Hepatocellular Cholangiocarcinoma Cirrhosis, Familial, with Pulmonary Hypertension Digestive System Diseases Digestive System Neoplasms Hepatocellular Carcinoma Intrahepatic Cholangiocarcinoma (Icc) Liver Diseases Liver Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Guangxi Medical University First Affiliated Hospital

Nanning, Guangxi, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification

Time frame: At the time of initial diagnosis

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.
Prognostic Performance of the Multimodal AI Model for Postoperative Survival Prediction

Time frame: minimum follow-up of 24 months

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.

Sponsors and collaborators

Lead sponsor

Guangxi Medical University

Other

Registry information

Official study title

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

Acronym: AIM-LCAP

Important dates

Study start: 2025
Primary completion: 2028
Study completion: 2028
First posted: Jun 22, 2026
Registry last updated: Jul 1, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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