An observational study in Liver Cancer, Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma (Icc), sponsored by Guangxi Medical University. Active, not recruiting at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2026-07-01.
Sponsored by Guangxi Medical University · Observational
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.
1,391 studies on the registry are indexed under Liver Neoplasms; 345 are open to participants now.
This study's planned enrollment of 600 is above the median of 200 across 350 observational studies indexed under Liver Neoplasms.
Browse Liver Neoplasms studies →Guangxi Medical University is the lead sponsor of 68 studies on the registry; 14 are open to participants now.
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(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.
-Diagnostic Model Cohort:
Prognostic Prediction Model Cohort (selected from diagnostic cohort):
Exclusion Criteria:
Key clinical, imaging, or pathological data severely missing or incomplete
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 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.
Diagnostic Accuracy of the Multimodal AI Model for Liver Lesion Classification
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.
Time frame: At the time of initial diagnosis
Prognostic Performance of the Multimodal AI Model for Postoperative Survival Prediction
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.
Time frame: minimum follow-up of 24 months
This study is active, not recruiting, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.
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Guangxi Medical University