Ireland
--:--IST
Latest
Astellas Expands Its 330 Million Euro Tralee Biopharma Facility with a Second Aseptic Filling Line to Double Drug-Product Capacity Xeolas Pharmaceuticals Opens 158,000 Sq Ft State-of-the-Art Baldoyle Facility to Scale Specialty Medicine Manufacturing Priya Life Science Partners with Fleming for the 9th Annual Corporate Compliance & Transparency in Life Sciences Summit in Zurich Ireland Has the Capital and the Lessons: Digital Project Management Is How They Become Delivery Dunbar Pharma Brings First Plant-Derived Dronabinol API to UK Market Through IPS Pharma Leveraging Priya Life Science as a Data Tracker: The Ultimate Use Case & Career Guide The €100K Reality Check: Why a Six-Figure Pharma Salary in Ireland Feels Different Than in Switzerland or Germany Ireland's €93.8 Billion Non-EU Pharma Export Engine: 2025 Eurostat Trade Data and Economic Impact Astellas Expands Its 330 Million Euro Tralee Biopharma Facility with a Second Aseptic Filling Line to Double Drug-Product Capacity Xeolas Pharmaceuticals Opens 158,000 Sq Ft State-of-the-Art Baldoyle Facility to Scale Specialty Medicine Manufacturing Priya Life Science Partners with Fleming for the 9th Annual Corporate Compliance & Transparency in Life Sciences Summit in Zurich Ireland Has the Capital and the Lessons: Digital Project Management Is How They Become Delivery Dunbar Pharma Brings First Plant-Derived Dronabinol API to UK Market Through IPS Pharma Leveraging Priya Life Science as a Data Tracker: The Ultimate Use Case & Career Guide The €100K Reality Check: Why a Six-Figure Pharma Salary in Ireland Feels Different Than in Switzerland or Germany Ireland's €93.8 Billion Non-EU Pharma Export Engine: 2025 Eurostat Trade Data and Economic Impact
Active, not recruiting Observational

AI Multimodal Model for Liver Cancer Diagnosis and Prognosis

NCT07658586 · tracked via the Priya Life Science China tracker
Phase
Observational
Started
2025-12-01
Last updated
2026-07-01

Condition(s) studied

Liver CancerHepatocellular CarcinomaIntrahepatic Cholangiocarcinoma (Icc)

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.

Eligibility

Sex
ALL
Min age
18 Years
Max age
80 Years
Healthy volunteers
No
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

Primary outcome measure(s)

  • Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification — 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 — 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.

Trial sites (1)

FacilityCityRegionStatus
Guangxi Medical University First Affiliated Hospital Nanning Guangxi
Official registry record

This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.

View NCT07658586 on ClinicalTrials.gov ↗ ← All trials in China