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AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer

NCT05816902 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2779

Last updated 2023-04-18

No results posted yet for this study

Summary

Background:

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

Method:

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. The optimization model with best performance was compared to the clinical predictor. In addition, stratified LM patients by risk score were utilized for survival analysis.

Conditions

Interventions

OTHER

The location of the patient's treatment

The location of the patient's treatment

Sponsors & Collaborators

  • The Second Affiliated Hospital of Harbin Medical University

    collaborator OTHER

  • Peking Union Medical College

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-01-01
Primary Completion
2020-12-31
Completion
2020-12-31

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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 NCT05816902 on ClinicalTrials.gov