An observational study in Liver Metastases, Gastric Adenocarcinoma and Colorectal Adenocarcinoma, sponsored by West China Hospital. Completed at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-13.
Sponsored by West China Hospital · Observational
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.
Liver metastasis represents the most common form of distant metastasis for digestive adenocarcinomas. Approximately 22% of patients with colorectal cancer and 52% of patients with pancreatic cancer present with liver metastasis at initial diagnosis. Two challenging clinical scenarios are recognized: participants with liver metastases derived from occult primary tumors (carcinoma of unknown primary, CUP), and participants with multiple primary malignancies in whom newly-detected hepatic lesions cannot be distinguished as metastatic deposits versus de-novo primary tumors. Pathological biopsy serves as the gold-standard for differential diagnosis; however, its invasiveness, sampling bias, and limited repeatability restrict routine clinical application. Prior radiomics and deep-learning investigations predominantly addressed binary-classification tasks and lacked multi-class external validation. This retrospective study will enroll approximately 500-700 eligible participants with pathologically-confirmed adenocarcinoma liver metastases. The investigators will extract radiomic features from pretreatment portal-venous-phase contrast-enhanced CT images, and construct and validate an integrated radiomics-deep-learning model for four-class primary-site differentiation. All imaging and clinical datasets will undergo full de-identification prior to analysis. No participant will receive novel clinical interventions for the purpose of this research.
West China Hospital is the lead sponsor of 498 studies on the registry; 244 are open to participants now.
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Adult patients with pathologically confirmed adenocarcinoma liver metastases from four primary gastrointestinal adenocarcinoma subtypes (gastric, colorectal, biliary/gallbladder, pancreatic ductal adenocarcinoma). Retrospectively enrolled from a single tertiary hospital.
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Exclusion Criteria:
This cohort includes patients with pathologically confirmed adenocarcinoma liver metastases from four primary digestive tumors (gastric, colorectal, biliary tract and gallbladder, pancreatic ductal adenocarcinoma). Retrospective collection of de-identified pretreatment contrast-enhanced CT images and clinical data. No study-related intervention is performed.
Other: Retrospective image analysis
No prospective intervention is applied to participants. This study only performs retrospective analysis of previously acquired contrast-enhanced CT images and clinical data, no new diagnostic or therapeutic procedures are performed for study purposes.
Overall classification accuracy of the combined radiomics-deep learning model for multi-class primary-site prediction of adenocarcinoma liver metastases
Proportion of correctly predicted primary tumor sites among all participants with pathologically-confirmed adenocarcinoma liver metastases, calculated from CT-based radiomics-deep-learning model predictions against reference pathological diagnosis.
Time frame: At baseline (time of the pretreatment contrast-enhanced CT scan)
Area under the receiver-operating-characteristic curve (AUC) of the radiomics-deep learning model for each primary tumor category
Model AUC values calculated separately for each of the four digestive adenocarcinoma primary tumor subtypes, using pathological diagnosis as reference standard.
Time frame: At baseline (time of the pretreatment contrast-enhanced CT scan)
Sensitivity and specificity of the radiomics-deep learning model for primary-site classification of each tumor subtype
Per-tumor-category sensitivity and specificity of the multi-class prediction model, with pathological diagnosis serving as ground-truth reference.
Time frame: At baseline (time of the pretreatment contrast-enhanced CT scan)
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Plan to share: Undecided
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is completed, as verified in Oct 2026. You cannot join it, but the record below documents what was studied.
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