Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher Ré, Daniel L. Rubin () and Michael Snyder ()
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Kun-Hsing Yu: Biomedical Informatics Program, Stanford University
Ce Zhang: Stanford University
Gerald J. Berry: Stanford University
Russ B. Altman: Biomedical Informatics Program, Stanford University
Christopher Ré: Stanford University
Daniel L. Rubin: Biomedical Informatics Program, Stanford University
Michael Snyder: Stanford University

Nature Communications, 2016, vol. 7, issue 1, 1-10

Abstract: Abstract Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients’ prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P

Date: 2016
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DOI: 10.1038/ncomms12474

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