Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

ABSTRACT:

Objective

Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively.

Materials and methods

We processed whole-slide histopathology images of lung adenocarcinoma (n = 427) and lung squamous cell carcinoma patients (n = 457) in the Cancer Genome Atlas. We built convolutional neural networks to classify histopathology images, evaluated their performance by the areas under the receiver-operating characteristi

SUBMITTER: Yu KH 

PROVIDER: S-EPMC7309263 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature