Development and validation of a machine learning model and nomogram for predicting brain metastasis in lung cancer: a population-based study.

ABSTRACT:

Objective

In advanced-stage lung cancer management, the brain emerges as the predominant metastatic site. Accurately forecasting both the likelihood of intracranial metastasis and patient survival duration has become a critical clinical priority, though existing predictive systems demonstrate insufficient precision.

Materials and method

This study conducted a retrospective cohort study utilizing SEER database records of lung cancer cases spanning 2010‒2015, incorporating demographic and clinical data. Multivariate logistic regression was initially employed for variable selection, followed by Cox proportional hazards modeling to identify risk factors associated with brain metastasis development and prognostic indicators. Significant predictors were subsequently integrated int

SUBMITTER: Zeng Y 

PROVIDER: S-EPMC12662121 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature