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

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.

Study Overview

• Status: Recruiting
• Conditions: Colorectal Cancer Liver Metastases (CRLM)
• Intervention / Treatment: Other: Multimodal Deep Learning Model Analysis

• Study Type: Observational
• Enrollment (Estimated): 1500

Contacts and Locations

• Study Contact: Name: Yang WU, M.D.; Phone Number: 13636076910; Email: 255001907@qq.com

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China
      • Recruiting
      • Tongji Hospital
      • Contact: Yang WU, M.D.; Phone Number: 13636076910; Email: 255001907@qq.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Liver Metastasis-Free Survival (LMFS) by Risk Group	The difference in liver metastasis-free survival between the high-risk and low-risk groups as stratified by the multimodal 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.	up to 3 years

Collaborators and Investigators

• Sponsor: Tongji Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2015
• Primary Completion (Actual): January 30, 2026
• Study Completion (Estimated): January 30, 2026

Study Registration Dates

• First Submitted: January 30, 2026
• First Submitted That Met QC Criteria: February 9, 2026
• First Posted (Actual): February 10, 2026

Study Record Updates

• Last Update Posted (Actual): February 10, 2026
• Last Update Submitted That Met QC Criteria: February 9, 2026
• Last Verified: January 1, 2026

More Information

Terms related to this study

• Keywords: predictive model; deep learning; multimodal; Colorectal cancer liver metastases (CRLM)
• Other Study ID Numbers: TJ-IRB202512239

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