AI-Based Prediction of Liver Metastasis in Colorectal Cancer (A Retrospective Study)

NCT07399236 | Recruiting

• 1 other identifier: TJ-IRB202512239
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief Summary

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

Colorectal Cancer Liver Metastases (CRLM)

Keywords

Colorectal cancer liver metastases (CRLM); deep learning; multimodal; predictive model

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC)

  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.

  up to 3 years

Secondary Outcomes (1)

• Liver Metastasis-Free Survival (LMFS) by Risk Group

  up to 3 years

Study Arms (1)

Colorectal Cancer Resection Cohort

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.

Other: Multimodal Deep Learning Model Analysis

Interventions

Multimodal Deep Learning Model Analysis OTHER

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.

Colorectal Cancer Resection Cohort

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients (aged 18-75) with stage I-III primary colorectal cancer who underwent curative resection at participating medical centers between 2015 and 2025, and for whom complete preoperative imaging, postoperative pathological data, and follow-up records are available for retrospective analysis.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Tongji Hospital: lead

Study Sites (1)

Tongji Hospital

Wuhan, Hubei, China

RECRUITING

Central Study Contacts

Yang wu, M.D.

CONTACT

13636076910; 255001907@qq.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: January 30, 2026
• First Posted: February 10, 2026
• Study Start: January 1, 2015
• Primary Completion: January 30, 2026
• Study Completion: January 30, 2026
• Last Updated: February 10, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)