- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT07392567
Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer
A Multicenter, Prospective, Observational Study for the Validation of a Multimodal Deep Learning Model to Predict Metachronous Liver Metastasis in Patients With Colorectal Cancer After Curative Resection
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.
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Yang WU, M.D.
- Phone Number: 13636076910
- Email: 255001907@qq.com
Study Locations
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Hubei
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Wuhan, Hubei, China
- Recruiting
- Tongji Hospital
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Principal Investigator:
- Wanguang Zhang, M.D.
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Contact:
- Yang WU, M.D.
- Phone Number: 13636076910
- Email: 255001907@qq.com
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-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Age 18-75 years, any gender.
- Clinical diagnosis of primary colon or rectal adenocarcinoma (Stage I-III). Scheduled to undergo curative radical 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 examination.
- ECOG Performance Status of 0 or 1.
- Patient or their legal representative voluntarily participates and provides written informed consent.
Exclusion Criteria:
- Postoperative pathological confirmation of non-primary colorectal adenocarcinoma or presence of distant metastasis.
- Intraoperative determination of non-R0 resection, or performance of palliative surgery/ostomy only.
- History of other malignant tumors.
- Previous history of liver surgery or liver transplantation.
- Death within the perioperative period (within 30 days after surgery).
- Refusal to participate in follow-up, withdrawal of informed consent, or loss to follow-up.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Prospective Validation Cohort
This single cohort consists of patients with stage I-III colorectal cancer who are prospectively enrolled after undergoing curative resection.
No interventions are administered as part of this study.
The cohort is used for the external validation of the pre-defined multimodal deep learning model's performance in predicting the risk of metachronous liver metastasis.
All patients receive standard of care treatment and follow-up according to clinical guidelines.
|
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.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Area Under the Receiver Operating Characteristic Curve (AUC)
Time Frame: 2 years after surgery
|
The discriminatory performance of the pre-specified multimodal deep learning model for predicting the occurrence of metachronous liver metastasis within 2 years after curative resection.
The model integrates preoperative contrast-enhanced CT, digital pathology, and clinical data.
Performance is evaluated on the entire prospectively enrolled validation cohort.
|
2 years after surgery
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Liver Metastasis-Free Survival (LMFS) by Risk Group
Time Frame: From the date of surgery until the date of first documented liver metastasis or last follow-up, assessed up to 3 years.
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The difference in liver metastasis-free survival between the high-risk and low-risk groups, as stratified by the model.
LMFS is defined as the time from surgery to the first radiological diagnosis of liver metastasis.
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From the date of surgery until the date of first documented liver metastasis or last follow-up, assessed up to 3 years.
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Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- TJ-IRB202601017
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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