Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data.

ABSTRACT: The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation of response patterns in a real-world setting. We propose a multivariate Bayesian joint model using generalized linear mixed effects, trained and validated on RWD from 424 advanced NSCLC patients retrospectively collected from three clinical centers. Center1 was used as training ( N=212 ), while Center2 and Center3 were used as independent testing sets ( N=137 and N=75 , respectively). Peripheral blood data (PBD) were collected at baseline and at three follow-up time points, alongside demographic and epidemiologic features. Six models were trained to predi

SUBMITTER: Ramos-Guerra AD 

PROVIDER: S-EPMC11861465 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature