Clinical Research Directory

CT Radiomics and Deep Learning-Based Non-Invasive Model for Tracing Primary Sites of Adenocarcinoma Liver Metastases: Automatic Differentiation of Multiple Primary Digestive Tumors

Sponsor: West China Hospital

Summary

This is a retrospective observational study. Liver metastasis occurs frequently in digestive-system cancers. Identification of the primary tumor source of liver metastasis may pose clinical challenges. Biopsy is an invasive procedure associated with potential sampling errors. This study aims to develop and validate a non-invasive artificial-intelligence model based on contrast-enhanced CT images. The model integrates CT radiomics and deep learning to automatically determine the primary origin of adenocarcinoma liver metastases, including gastric adenocarcinoma, colorectal adenocarcinoma, biliary/gallbladder adenocarcinoma, and pancreatic ductal adenocarcinoma. The investigators will retrieve historical medical records and de-identified abdominal contrast-enhanced CT images from patients diagnosed at West China Hospital, Sichuan University between January 2015 and August 2026. No new intervention or treatment will be administered to participants in this study.

Official title: Non-Invasive Origin Tracing Model for Hepatic Metastases From Adenocarcinoma Based on CT Radiomics and Deep Learning: A Clinical Study for Automatic Differentiation of Multiple Primary Digestive Malignancies

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