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

NCT07399236 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2026-02-10

No results posted yet for this study

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)

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.

Sponsors & Collaborators

  • Tongji Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-01-01
Primary Completion
2026-01-30
Completion
2026-01-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07399236 on ClinicalTrials.gov