Colorectal Cancer, Lung Metastases
Conditions
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.
Interventions
OTHERThe location of the patient's treatment
The location of the patient's treatment
Sponsors
The Second Affiliated Hospital of Harbin Medical University
Peking Union Medical College
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Inclusion criteria
* patients with pathologic confirmation of a primary CRC diagnosis
Exclusion criteria
* (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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| lung metastasis | through study completion, an average of 3 month | diagnosed with lung metastasis |
Outcome results
None listed