Development and validation of an interpretable machine learning model for predicting progression-free survival after immunotherapy in patients with non-small cell lung cancer: a multicenter study.

ABSTRACT:

Background

This study aimed to develop and validate an interpretable machine learning model that harnesses circulating tumor DNA (ctDNA) to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing immunotherapy, thereby addressing the inherent limitations of conventional biomarkers such as PD-L1 expression and tumor mutational burden.

Methods

This multicenter study involved pretreatment ctDNA profiling of 441 patients with non-small cell lung cancer (NSCLC), stratified into three independent cohorts: a training set (n=303, OAK trial), a validation set (n=97, POPLAR trial), and a local test set (n=41, multicenter retrospective cohort, 2023-2024). Using 5-fold cross-validated LASSO-Cox (Least Absolute Shrinkage and Selection Operat

SUBMITTER: Li Y 

PROVIDER: S-EPMC12757269 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature