AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer
- Conditions
- Lung MetastasesColorectal Cancer
- Registration Number
- NCT05816902
- Lead Sponsor
- Peking Union Medical College
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 2779
- patients with pathologic confirmation of a primary CRC diagnosis
- (1) patients with multiple primary cancers or other malignancies; (2) patients identified via autopsy or death certificate; and (3) patients with uncertain clinical data values
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method lung metastasis through study completion, an average of 3 month diagnosed with lung metastasis
- Secondary Outcome Measures
Name Time Method