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

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...

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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
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Science
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Acceso en línea:https://doaj.org/article/05d507904fe140cd941a3a33245b3716
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Sumario: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.

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