Predicting cancer outcomes from histology and genomics using convolutional networks

Pooya Mobadersany; Safoora Yousefi; Mohamed Amgad; David A. Gutman; Jill S. Barnholtz-Sloan; Jose Enrique Velazquez Vega; Daniel J. Brat; Lee A.D. Cooper

doi:10.1101/198010

Predicting cancer outcomes from histology and genomics using convolutional networks

Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A. Gutman, Jill S. Barnholtz-Sloan, Jose Enrique Velazquez Vega, Daniel J. Brat, Lee A.D. Cooper^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

Original language	English (US)
Journal	Unknown Journal
DOIs	https://doi.org/10.1101/198010
State	Published - Oct 3 2017
Externally published	Yes

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
Immunology and Microbiology(all)
Neuroscience(all)
Pharmacology, Toxicology and Pharmaceutics(all)

Access to Document

10.1101/198010

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title = "Predicting cancer outcomes from histology and genomics using convolutional networks",

abstract = "Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.",

author = "Pooya Mobadersany and Safoora Yousefi and Mohamed Amgad and Gutman, {David A.} and Barnholtz-Sloan, {Jill S.} and {Velazquez Vega}, {Jose Enrique} and Brat, {Daniel J.} and Cooper, {Lee A.D.}",

note = "Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2017",

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journal = "Free Radical Biology and Medicine",

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AU - Yousefi, Safoora

AU - Amgad, Mohamed

AU - Gutman, David A.

AU - Barnholtz-Sloan, Jill S.

AU - Velazquez Vega, Jose Enrique

AU - Brat, Daniel J.

AU - Cooper, Lee A.D.

N1 - Publisher Copyright: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2017/10/3

Y1 - 2017/10/3

N2 - Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

AB - Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

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