Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

ABSTRACT: The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteris

SUBMITTER: Yu D 

PROVIDER: S-EPMC11217297 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature