NCT07658586Active, not recruiting
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
Source: ClinicalTrials.gov · Last updated 1 Jul 2026
Brief summary (registry, in English)
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.
- Status (registry)
- Active, not recruiting
- Study type
- Observational
- Enrollment
- 600 (estimated)
- First submitted
- 14 Jun 2026
- Start date
- 1 Dec 2025
- Primary completion date
- 1 Dec 2028
- Lead sponsor
- Guangxi Medical University
- Diseases
- Liver and biliary tract cancer
- NCT number
- NCT07658586
What you commit to
- Study type: Observational
- Planned participants: 600
- From 1 Dec 2025 to the primary completion date, 1 Dec 2028
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- · 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
Who the study is looking for
- -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)
Age in the registry: 18 years to 80 years · Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Guangxi Medical University First Affiliated Hospital Coordinator of this site? Confirm its status | Nanning | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.