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Recruiting NCT07392567

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

— Observational Colorectal Cancer Liver Metastasis

For patients and families

In plain language

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 Prediction Model.
Who it may be relevant to
Registry conditions: Colorectal Cancer Liver Metastasis. 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, 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

Overview

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.

Interventions

  • 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.

Primary outcome measures

  • Area Under the Receiver Operating Characteristic Curve (AUC) [Time frame: 2 years after surgery]
Secondary outcome measures (1)
  • 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.]

Eligibility criteria

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.

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

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Tongji Hospital — Wuhan

Identifiers

NCT: NCT07392567 · TJ-IRB202601017

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗