Integrated noninvasive diagnostics for prediction of survival in immunotherapy.

ABSTRACT:

Background

Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy.

Patients and methods

The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained

SUBMITTER: Yeghaian M 

PROVIDER: S-EPMC11342748 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature