Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer
NCT07392567 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 160
Last updated 2026-02-06
Summary
This is a prospective, multicenter, observational study designed to validate the predictive accuracy of a pre-developed multimodal deep learning model. The model integrates preoperative contrast-enhanced CT scans, digitized postoperative pathology images, and standard clinical data to estimate the risk of liver metastasis within two years after curative surgery in patients with stage I-III colorectal cancer.
The primary objective is to evaluate the model's performance in an independent, prospectively enrolled patient cohort. Participants will receive standard-of-care treatment according to clinical guidelines. The study involves no experimental interventions; it solely involves the collection and analysis of routinely generated clinical data. The goal is to assess the model's potential for clinical translation by providing a reliable tool for stratifying patients' risk of liver metastasis, which could inform personalized surveillance strategies.
Conditions
- Colorectal Cancer Liver Metastasis
Interventions
- DIAGNOSTIC_TEST
-
Multimodal Deep Learning Prediction Model
This is a non-therapeutic, prognostic study. The intervention under investigation is the application of a pre-specified multimodal deep learning model that integrates preoperative CT imaging, digital pathology, and clinical data to stratify patients' risk of developing metachronous liver metastasis. This model functions as a prognostic tool and is not used to guide patient management in this study. Its performance is being evaluated prospectively against the actual clinical outcomes.
Sponsors & Collaborators
-
Tongji Hospital
lead OTHER
Eligibility
- Min Age
- 18 Years
- Max Age
- 75 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2026-01-30
- Primary Completion
- 2028-01-30
- Completion
- 2029-01-30
Countries
- China
Study Locations
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