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AI Multimodal Model for Liver Cancer Diagnosis and Prognosis

NCT07658586 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2026-07-01

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

  • Guangxi Medical University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-12-01
Primary Completion
2028-12-01
Completion
2028-12-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07658586 on ClinicalTrials.gov