Clinical trial · Observational
AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colorectal Cancer Liver Metastases (CRLM) | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Multimodal Deep Learning Model Analysis | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Colorectal Cancer Resection Cohort
- description
- A retrospective cohort of adult patients (aged 18-75) with stage I-III primary colorectal adenocarcinoma who underwent curative (R0) resection. This cohort is defined for the purpose of developing and validating a multimodal deep learning model to predict the risk of metachronous liver metastasis. All data, including preoperative contrast-enhanced CT scans, postoperative digitized pathology slides, and clinical records, were collected retrospectively from routine clinical practice. No interventions were administered as part of this study.
- interventionNames
- Other: Multimodal Deep Learning Model Analysis
Primary outcomes (1)
- measure
- Area Under the Receiver Operating Characteristic Curve (AUC)
- timeFrame
- up to 3 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available