AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Multimodal Deep Learning Model Analysis.
- Who it may be relevant to
- Registry conditions: Colorectal Cancer Liver Metastases (CRLM). Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Multicenter, Retrospective, Observational Study to Develop and Validate a Multimodal Deep Learning Model for Predicting Metachronous Liver Metastasis in Colorectal Cancer Patients After Curative Resection
Overview
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.
Interventions
- Other Multimodal Deep Learning Model Analysis
This 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.
Primary outcome measures
- Area Under the Receiver Operating Characteristic Curve (AUC) [Time frame: up to 3 years]
Secondary outcome measures (1)
- Liver Metastasis-Free Survival (LMFS) by Risk Group [Time frame: up to 3 years]
Eligibility criteria
Inclusion criteria
- 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.
- No evidence of distant metastasis (including synchronous liver metastasis) on preoperative or intraoperative exploration.
Exclusion criteria
- 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).
- Lack of regular follow-up information.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Tongji Hospital — Wuhan
Identifiers
NCT: NCT07399236 · TJ-IRB202512239