EligibleTrials

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

Recruiting · NCT07399236 · Observational (researchers observe without assigning treatment) · Lead sponsor: Tongji Hospital

View the official record on ClinicalTrials.gov →
Colorectal cancer

Interventions studied

Multimodal Deep Learning Model Analysis

What this trial is 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.

Who can take part

Age range
18 Years to 75 Years
Sex
All (male and female)
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)

Inclusion criteria

Exclusion criteria

Where it is running

1 location listed.

Read the full protocol, contacts and eligibility on ClinicalTrials.gov →

Get notified when new trials open

One email when new recruiting trials are added. Unsubscribe anytime.