Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer.

ABSTRACT: Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with ES-SCLC between 2010 and 2018 were screened from the Surveillance, Epidemiology, and End Results (SEER) database. Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. The accuracy, sensitivity, specificity, AUROC, and AUPRC were compared among these models and traditional logistic regression (LR). The random forest (RF) model demonstrated the best performance and was chosen for further analysis. The AUROC and AUPRC were calculated and compared. The findings from the RF model were util

SUBMITTER: Munai E 

PROVIDER: S-EPMC11579493 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature