Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks (original) (raw)

Journal Article

,

Department of Biomedical Informatics

, Harvard Medical School, Boston, Massachusetts,

USA

Corresponding Authors: Kun-Hsing Yu, MD, PhD, Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Fourth Floor, Boston, MA 02115, USA; Kun-Hsing_Yu@hms.harvard.edu

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Department of Electrical Engineering

, Stanford University, Stanford, California,

USA

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Department of Pathology

, Stanford University, Stanford, California,

USA

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Department of Computer Science

, Stanford University, Stanford, California,

USA

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Biomedical Informatics Program

, Stanford University, Stanford, California,

USA

Department of Bioengineering

, Stanford University, Stanford, California,

USA

Department of Genetics

, Stanford University, Stanford, California,

USA

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Department of Genetics

, Stanford University, Stanford, California,

USA

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Department of Biomedical Informatics

, Harvard Medical School, Boston, Massachusetts,

USA

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Revision received:

22 November 2019

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Kun-Hsing Yu, Feiran Wang, Gerald J Berry, Christopher Ré, Russ B Altman, Michael Snyder, Isaac S Kohane, Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks, Journal of the American Medical Informatics Association, Volume 27, Issue 5, May 2020, Pages 757–769, https://doi.org/10.1093/jamia/ocz230
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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 characteristic curves (AUCs), and validated the results in an independent cohort (n = 125).

Results

To establish neural networks for quantitative image analyses, we first built convolutional neural network models to identify tumor regions from adjacent dense benign tissues (AUCs > 0.935) and recapitulated expert pathologists’ diagnosis (AUCs > 0.877), with the results validated in an independent cohort (AUCs = 0.726-0.864). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < .01).

Discussion

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.

© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com

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