Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.

ABSTRACT: Despite promising results in using deep learning to infer genetic features from histological whole-slide images (WSIs), no prior studies have specifically applied these methods to lung adenocarcinomas from subjects who have never smoked tobacco (NS-LUAD) - a molecularly and histologically distinct subset of lung cancer. Existing models have focused on LUAD from predominantly smoker populations, with limited molecular scope and variable performance. Here, we propose a customized deep convolutional neural network based on ResNet50 architecture, optimized for multilabel classification for NS-LUAD, enabling simultaneous prediction of 16 molecular alterations from a single H&E-stained WSI. Key architectural modifications included a simplified two-layer residual block without bottleneck layers,

SUBMITTER: Saha M 

PROVIDER: S-EPMC12393265 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature