Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	James A. Diao, Jason K. Wang, Wan Fung Chui, Victoria Mountain, Sai Chowdary Gullapally, Ramprakash Srinivasan, Richard N. Mitchell, Benjamin Glass, Sara Hoffman, Sudha K. Rao, Chirag Maheshwari, Abhik Lahiri, Aaditya Prakash, Ryan McLoughlin, Jennifer K. Kerner, Murray B. Resnick, Michael C. Montalto, Aditya Khosla, Ilan N. Wapinski, Andrew H. Beck, Hunter L. Elliott, Amaro Taylor-Weiner
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/05d507904fe140cd941a3a33245b3716
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:05d507904fe140cd941a3a33245b3716
record_format	dspace
spelling	oai:doaj.org-article:05d507904fe140cd941a3a33245b37162021-12-02T13:30:36ZHuman-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes10.1038/s41467-021-21896-92041-1723https://doaj.org/article/05d507904fe140cd941a3a33245b37162021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21896-9https://doaj.org/toc/2041-1723Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features.James A. DiaoJason K. WangWan Fung ChuiVictoria MountainSai Chowdary GullapallyRamprakash SrinivasanRichard N. MitchellBenjamin GlassSara HoffmanSudha K. RaoChirag MaheshwariAbhik LahiriAaditya PrakashRyan McLoughlinJennifer K. KernerMurray B. ResnickMichael C. MontaltoAditya KhoslaIlan N. WapinskiAndrew H. BeckHunter L. ElliottAmaro Taylor-WeinerNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q James A. Diao Jason K. Wang Wan Fung Chui Victoria Mountain Sai Chowdary Gullapally Ramprakash Srinivasan Richard N. Mitchell Benjamin Glass Sara Hoffman Sudha K. Rao Chirag Maheshwari Abhik Lahiri Aaditya Prakash Ryan McLoughlin Jennifer K. Kerner Murray B. Resnick Michael C. Montalto Aditya Khosla Ilan N. Wapinski Andrew H. Beck Hunter L. Elliott Amaro Taylor-Weiner Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
description	Computational methods have made progress in improving classification accuracy and throughput of pathology workflows, but lack of interpretability remains a barrier to clinical integration. Here, the authors present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features.
format	article
author	James A. Diao Jason K. Wang Wan Fung Chui Victoria Mountain Sai Chowdary Gullapally Ramprakash Srinivasan Richard N. Mitchell Benjamin Glass Sara Hoffman Sudha K. Rao Chirag Maheshwari Abhik Lahiri Aaditya Prakash Ryan McLoughlin Jennifer K. Kerner Murray B. Resnick Michael C. Montalto Aditya Khosla Ilan N. Wapinski Andrew H. Beck Hunter L. Elliott Amaro Taylor-Weiner
author_facet	James A. Diao Jason K. Wang Wan Fung Chui Victoria Mountain Sai Chowdary Gullapally Ramprakash Srinivasan Richard N. Mitchell Benjamin Glass Sara Hoffman Sudha K. Rao Chirag Maheshwari Abhik Lahiri Aaditya Prakash Ryan McLoughlin Jennifer K. Kerner Murray B. Resnick Michael C. Montalto Aditya Khosla Ilan N. Wapinski Andrew H. Beck Hunter L. Elliott Amaro Taylor-Weiner
author_sort	James A. Diao
title	Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
title_short	Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
title_full	Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
title_fullStr	Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
title_full_unstemmed	Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
title_sort	human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05d507904fe140cd941a3a33245b3716
work_keys_str_mv	AT jamesadiao humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT jasonkwang humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT wanfungchui humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT victoriamountain humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT saichowdarygullapally humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT ramprakashsrinivasan humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT richardnmitchell humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT benjaminglass humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT sarahoffman humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT sudhakrao humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT chiragmaheshwari humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT abhiklahiri humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT aadityaprakash humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT ryanmcloughlin humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT jenniferkkerner humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT murraybresnick humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT michaelcmontalto humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT adityakhosla humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT ilannwapinski humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT andrewhbeck humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT hunterlelliott humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes AT amarotaylorweiner humaninterpretableimagefeaturesderivedfromdenselymappedcancerpathologyslidespredictdiversemolecularphenotypes
_version_	1718392894151196672

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Ejemplares similares