Quality-of-life scale machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer.

ABSTRACT:

Background

Despite immune checkpoint inhibitors(ICIs) significantly improve clinical outcomes in patients with advanced non-small cell lung cancer (aNSCLC), disease progression is inevitable. A diverse patient-reported Quality-of-life(QoL) scales were used to predict outcomes for aNSCLC patients with atezolizumab using machine learning.

Materials and methods

This study analyzed the association between baseline QoL and clinical outcomes in aNSCLC patients with atezolizumab in 4 randomized clinical trials: the IMpower150 study (discovery cohort), the BIRCH, OAK and POPLAR study (validation cohorts). We identified quality of life subtypes (QoLS) by consensus clustering in the discovery cohort and predicted them in external validated cohorts.

Results

We identified QoLS1 a

SUBMITTER: Shen J 

PROVIDER: S-EPMC12314532 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature