Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning.

ABSTRACT:

Background

Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.

Materials and methods

A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected.

SUBMITTER: Liao CY 

PROVIDER: S-EPMC11440728 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature