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

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 27; no. 5; pp. 757 - 769
• Main Authors: Yu, Kun-Hsing, Wang, Feiran, Berry, Gerald J, Ré, Christopher, Altman, Russ B, Snyder, Michael, Kohane, Isaac S
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.05.2020
• Subjects: Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - pathology; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Squamous Cell - genetics; Carcinoma, Squamous Cell - pathology; Humans; Lung Neoplasms - genetics; Lung Neoplasms - pathology; Machine Learning; Neural Networks, Computer; Research and Applications; ROC Curve; Transcriptome; non-small cell lung cancer; quantitative pathology; convolutional neural networks; machine learning; transcriptomic subtypes

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, w...