Recruiting NCT07399236 Last updated by the registry: 2026-02-10

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

— Observational Colorectal Cancer Liver Metastases (CRLM)

Why this appeared in your results: Colorectal Cancer Liver Metastases (CRLM) · Recruiting · China

Detailed eligibility, including treatment history and laboratory results, still needs to be checked.

For patients and families

In plain language

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An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Multimodal Deep Learning Model Analysis.
Who it may be relevant to
Registry conditions: Colorectal Cancer Liver Metastases (CRLM). Basic parameters: 18 years — 75 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title

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

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • Area Under the Receiver Operating Characteristic Curve (AUC) [Time frame: up to 3 years]
Secondary outcome measures (1)
  • Liver Metastasis-Free Survival (LMFS) by Risk Group [Time frame: up to 3 years]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Questions to ask your doctor

  • Is this trial appropriate for my diagnosis and stage?
  • What is known about the study treatment and its risks?
  • Would participation interfere with my current treatment?
  • Which tests are needed to check the eligibility criteria?
  • Are there similar trials closer to home?

What information a coordinator may need

  • the exact diagnosis and stage (briefly, as in the medical report);
  • previous and current treatment with dates;
  • recent tests and reports (CT/MRI/PET, pathology) — when the coordinator asks, not in the first request;
  • age, country of residence and ability to travel;
  • the NCT number of this trial (filled in automatically when you apply from this page).

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Tongji Hospital — Wuhan

Identifiers

NCT: NCT07399236 · TJ-IRB202512239

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗

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