Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma.

ABSTRACT:

Background

Concurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways.

Methods

Overall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing.

Results

The DL model exhibited favo

SUBMITTER: Peng J 

PROVIDER: S-EPMC11451157 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature