Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer

January 30, 2026 updated by: Wan-Guang Zhang, Tongji Hospital

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

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

160

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China
        • Recruiting
        • Tongji Hospital
        • Principal Investigator:
          • Wanguang Zhang, M.D.
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

This study population consists of adult patients (aged 18-75) with newly diagnosed, stage I-III primary colorectal cancer who are scheduled to undergo curative resection at one of the participating clinical centers. This prospective cohort will be used for the independent validation of a pre-developed multimodal deep learning model designed to predict the risk of metachronous liver metastasis. All participants will provide informed consent prior to enrollment.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

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.
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.

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

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.
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.
From the date of surgery until the date of first documented liver metastasis or last follow-up, assessed up to 3 years.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tongji Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 30, 2026

Primary Completion (Estimated)

January 30, 2028

Study Completion (Estimated)

January 30, 2029

Study Registration Dates

First Submitted

January 30, 2026

First Submitted That Met QC Criteria

January 30, 2026

First Posted (Actual)

February 6, 2026

Study Record Updates

Last Update Posted (Actual)

February 6, 2026

Last Update Submitted That Met QC Criteria

January 30, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • TJ-IRB202601017

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Colorectal Cancer Liver Metastasis

Clinical Trials on Multimodal Deep Learning Prediction Model

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Greece

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe