Nature Communications (Mar 2021)

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

  • 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

DOI
https://doi.org/10.1038/s41467-021-21896-9
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 15

Abstract

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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.