Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy.

ABSTRACT:

Objective

Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.

Methods and analysis

We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve

SUBMITTER: Zhou JG 

PROVIDER: S-EPMC11203083 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature