Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk.

ABSTRACT:

Background

Radiomics based on computed tomography (CT) images is potential in promoting individualized treatment of non-small cell lung cancer (NSCLC), however, its role in immunotherapy needs further exploration. The aim of this study was to develop a CT-based radiomics score to predict the efficacy of immune checkpoint inhibitor (ICI) monotherapy in patients with advanced NSCLC.

Methods

Two hundred and thirty-six ICI-treated patients were retrospectively included and divided into a training cohort (n=188) and testing cohort (n=48) at a ratio of 8 to 2. The efficacy outcomes of ICI were evaluated based on overall survival (OS) and progression-free survival (PFS). We designed a survival network and combined it with a Cox regression model to obtain patients' OS risk score (OS

SUBMITTER: He BX 

PROVIDER: S-EPMC9073735 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature