Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

ABSTRACT:

Background

Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without mutations, but drug resistance often reduces effectiveness. Developing more effective methods to predict and monitor chemotherapy benefits early is crucial.

Methods

We carried out a retrospective cohort study of NSCLC patients without targeted mutations who received chemotherapy at West China Hospital from 2009 to 2013. We identified variables associated with chemotherapy outcomes and built four predictive models by machine learning. Shapley additive explanations (SHAP) interpreted the best model's predictions. The Kaplan-Meier method assessed key variables' impact on 5-year overall survival.

Results

The study enrolled 461 NS

SUBMITTER: Shuang Z 

PROVIDER: S-EPMC11655385 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature