A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas.

ABSTRACT:

Background

Programmed death ligand-1 (PD-L1) expression remains a crucial predictor in selecting patients for immunotherapy. The current study aimed to non-invasively predict PD-L1 expression based on chest computed tomography (CT) images in advanced lung adenocarcinomas (LUAD), thus help select optimal patients who can potentially benefit from immunotherapy.

Methods

A total of 127 patients with stage III and IV LUAD were enrolled into this study. Pretreatment enhanced thin-section CT images were available for all patients and were analyzed in terms of both morphologic characteristics by radiologists and deep learning (DL), so to further determine the association between CT features and PD-L1 expression status. Univariate analysis and multivariate logical regression analysis

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC7475404 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature