AI Multimodal Model for Liver Cancer Diagnosis and Prognosis (AIM-LCAP)

June 28, 2026 updated by: Fubo Wang, Guangxi Medical University

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.

Study Overview

Status

Active, not recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

600

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Guangxi
      • Nanning, Guangxi, China
        • Guangxi Medical University First Affiliated Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

(1) Key clinical, imaging, or pathological data severely missing or incomplete; (2) Preoperative CT or MRI images of poor quality or missing sequences, unable to perform reliable image analysis; (3) Prior local treatment for the target liver lesion, unless clearly recorded as neoadjuvant therapy before surgery; (4) Concurrent other malignant tumors; (5) Lost to follow-up or follow-up data cannot meet endpoint determination requirements.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Diagnostic
Diagnostic Model Cohort: Patients with suspected liver space-occupying lesions who underwent preoperative contrast-enhanced CT or MRI and have definite pathological diagnosis (surgical or biopsy) as gold standard.
Prognostic
Prognostic Prediction Model Cohort: Patients selected from the diagnostic cohort who were pathologically diagnosed with liver cancer, received radical hepatectomy, and have complete postoperative follow-up data (minimum 24 months) to determine progression-free survival and overall survival endpoints.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
At the time of initial diagnosis
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.
minimum follow-up of 24 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Guangxi Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2025

Primary Completion (Estimated)

December 1, 2028

Study Completion (Estimated)

December 1, 2028

Study Registration Dates

First Submitted

June 14, 2026

First Submitted That Met QC Criteria

June 14, 2026

First Posted (Actual)

June 22, 2026

Study Record Updates

Last Update Posted (Actual)

July 1, 2026

Last Update Submitted That Met QC Criteria

June 28, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KY20260007

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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