AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-75
SponsorTongji Hospital
About this trial
This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. The model will integrate preoperative contrast-enhanced CT imaging, digitized histopathological whole-slide images, and standard clinical-pathological data.
The primary objective is to assess the model's discriminatory performance, measured by the area under the receiver operating characteristic curve (AUC), and to compare its predictive accuracy against traditional prognostic factors such as TNM staging and serum carcinoembryonic antigen levels. This research utilizes existing archival data; no direct patient contact or intervention is involved. The ultimate goal is to provide a robust, data-driven tool for improved risk stratification, which could potentially guide personalized surveillance strategies and adjuvant therapy decisions in the future.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age 18-75 years, any gender.
Histologically confirmed primary colon or rectal adenocarcinoma.
Underwent curative radical resection (R0 resection) for colorectal cancer.
Preoperative contrast-enhanced abdominal/pelvic CT scan performed within 1 month before surgery, with acceptable image quality.
Disqualifiers
History of other malignant tumors.
Previous history of liver surgery or liver transplantation.
Missing clinical, imaging, or pathological data required for the study.
Death within the perioperative period (within 30 days after surgery).
Trial population
Adult patients (aged 18-75) with stage I-III primary colorectal cancer who underwent curative resection at participating medical centers between 2015 and 2025, and for whom complete preoperative imaging, postoperative pathological data, and follow-up records are available for retrospective analysis.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Multimodal Deep Learning Model Analysis
Other interventionThis is a non-interventional study. The primary study procedure is the application of a multimodal deep learning model to retrospectively analyze existing clinical data (contrast-enhanced CT images, digitized pathology slides, and structured clinical variables) for the purpose of predicting the risk of metachronous liver metastasis. No therapeutic or diagnostic interventions are administered to participants as part of this research protocol.
Treatment groups
1,500 Participants
are divided into 1 treatment groupGroup A: Colorectal Cancer Resection Cohort1 intervention
Trial outcomes
Primary outcomes
1
Area Under the Receiver Operating Characteristic Curve (AUC)
The discriminatory performance of the multimodal deep learning model for predicting the 3-year risk of metachronous liver metastasis. The model integrates preoperative contrast-enhanced CT images, digitized whole-slide pathology images, and clinical data. The AUC will be calculated on the held-out independent test set. The assessment is based on data collected from the date of curative surgery (baseline) to the date of first imaging-confirmed liver metastasis or last follow-up.
Time frame
up to 3 yearsSecondary outcomes
1
Liver Metastasis-Free Survival (LMFS) by Risk Group
The difference in liver metastasis-free survival between the high-risk and low-risk groups as stratified by the multimodal model. LMFS is defined as the time from surgery to the first radiological diagnosis of liver metastasis. From the date of surgery until the date of first documented liver metastasis or last follow-up.
Time frame
up to 3 yearsSponsors and contacts
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Source ClinicalTrials.gov