Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images.

Journal: Cancer research communications
Published Date: Jan 1, 2025

Abstract

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.

Authors

  • Nikhil Cherian Kurian
    Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
  • Peter H Gann
    Department of Pathology, University of Illinois at Chicago, Chicago, IL, USA.
  • Neeraj Kumar
  • Stephanie M McGregor
    Department of Pathology and Laboratory Medicine, University of Wisconsin Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin.
  • Ruchika Verma
  • Amit Sethi

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