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

Tongji Hospital

Status: Enrolling

Conditions: Colorectal Cancer Liver Metastases (CRLM)

Treatments: Other: Multimodal Deep Learning Model Analysis

Study type: Observational

Funder types: Other

Identifiers

NCT07399236

TJ-IRB202512239

Details and patient eligibility

About

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.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

1,500 participants in 1 patient group

Colorectal Cancer Resection Cohort

Description:

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.

Treatment:

Other: Multimodal Deep Learning Model Analysis