Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains

Determination of estrogen receptor status (ERS) in breast cancer tissue requires immunohistochemistry, which is sensitive to the vagaries of sample processing and the subjectivity of pathologists. Here the authors present a deep learning model that determines ERS from H&E stained tissue, which c...

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Main Authors: Nikhil Naik, Ali Madani, Andre Esteva, Nitish Shirish Keskar, Michael F. Press, Daniel Ruderman, David B. Agus, Richard Socher
Format: Article
Language:English
Published: Nature Publishing Group 2020-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-19334-3
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spelling doaj-169f3fde02b44f04a44160ad8fb944192021-05-11T08:37:56ZengNature Publishing GroupNature Communications2041-17232020-11-011111810.1038/s41467-020-19334-3Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stainsNikhil Naik0Ali Madani1Andre Esteva2Nitish Shirish Keskar3Michael F. Press4Daniel Ruderman5David B. Agus6Richard Socher7Salesforce ResearchSalesforce ResearchSalesforce ResearchSalesforce ResearchDepartment of Pathology, Keck School of Medicine, University of Southern CaliforniaLawrence J. Ellison Institute for Transformative Medicine, University of Southern CaliforniaLawrence J. Ellison Institute for Transformative Medicine, University of Southern CaliforniaSalesforce ResearchDetermination of estrogen receptor status (ERS) in breast cancer tissue requires immunohistochemistry, which is sensitive to the vagaries of sample processing and the subjectivity of pathologists. Here the authors present a deep learning model that determines ERS from H&E stained tissue, which could improve oncology decisions in under-resourced settings.https://doi.org/10.1038/s41467-020-19334-3
collection DOAJ
language English
format Article
sources DOAJ
author Nikhil Naik
Ali Madani
Andre Esteva
Nitish Shirish Keskar
Michael F. Press
Daniel Ruderman
David B. Agus
Richard Socher
spellingShingle Nikhil Naik
Ali Madani
Andre Esteva
Nitish Shirish Keskar
Michael F. Press
Daniel Ruderman
David B. Agus
Richard Socher
Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
Nature Communications
author_facet Nikhil Naik
Ali Madani
Andre Esteva
Nitish Shirish Keskar
Michael F. Press
Daniel Ruderman
David B. Agus
Richard Socher
author_sort Nikhil Naik
title Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
title_short Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
title_full Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
title_fullStr Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
title_full_unstemmed Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains
title_sort deep learning-enabled breast cancer hormonal receptor status determination from base-level h&e stains
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-11-01
description Determination of estrogen receptor status (ERS) in breast cancer tissue requires immunohistochemistry, which is sensitive to the vagaries of sample processing and the subjectivity of pathologists. Here the authors present a deep learning model that determines ERS from H&E stained tissue, which could improve oncology decisions in under-resourced settings.
url https://doi.org/10.1038/s41467-020-19334-3
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