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Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks

View ORCID ProfileKun-Hsing Yu, Feiran Wang, Gerald J. Berry, Christopher Ré, Russ B. Altman, Michael Snyder, Isaac S. Kohane
doi: https://doi.org/10.1101/530360
Kun-Hsing Yu
1Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Fourth Floor, Boston, MA 02115
MD,PhD
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  • ORCID record for Kun-Hsing Yu
Feiran Wang
2Department of Electrical Engineering, Stanford University 350 Serra Mall, Stanford, CA 94305
MS
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Gerald J. Berry
3Department of Pathology, Stanford University 300 Pasteur Dr., L235, Stanford, CA 94305
MD
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Christopher Ré
4Department of Computer Science, Stanford University 353 Serra Mall, Stanford, CA 94305
PhD
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Russ B. Altman
5Biomedical Informatics Program, Stanford University 1265 Welch Road, MSOB, X-215, MC 5479, Stanford, CA 94305-5479, USA
6Department of Bioengineering, Stanford University 443 Via Ortega, Stanford, CA 94305-4125, USA
7Department of Genetics, Stanford University 300 Pasteur Dr., M-344, Stanford, CA 94305-5120, USA
MD,PhD
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Michael Snyder
7Department of Genetics, Stanford University 300 Pasteur Dr., M-344, Stanford, CA 94305-5120, USA
PhD
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Isaac S. Kohane
1Department of Biomedical Informatics, Harvard Medical School 10 Shattuck Street, Fourth Floor, Boston, MA 02115
MD,PhD
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Abstract

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 gene expression subtypes of non-small cell lung cancer objectively. We processed whole-slide histopathology images of lung adenocarcinoma (n=427) and lung squamous cell carcinoma patients (n=457) in The Cancer Genome Atlas. To establish neural networks for quantitative image analyses, we first build convolutional neural network models to identify tumor regions from adjacent dense benign tissues (areas under the receiver operating characteristic curves (AUC) > 0.935) and recapitulated expert pathologists’ diagnosis (AUC > 0.88), with the results validated in an independent cohort (n=125; AUC > 0.85). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < 0.01). Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully-automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically-relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.

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Posted January 25, 2019.
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Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks
Kun-Hsing Yu, Feiran Wang, Gerald J. Berry, Christopher Ré, Russ B. Altman, Michael Snyder, Isaac S. Kohane
bioRxiv 530360; doi: https://doi.org/10.1101/530360
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Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks
Kun-Hsing Yu, Feiran Wang, Gerald J. Berry, Christopher Ré, Russ B. Altman, Michael Snyder, Isaac S. Kohane
bioRxiv 530360; doi: https://doi.org/10.1101/530360

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