Deep learning predicts chromosomal instability from histopathology images

Authors

Zhuoran Xu, Weill Cornell Medical College
Akanksha Verma, Weill Cornell Medical College
Uska Naveed, Weill Cornell Medical College
Samuel F. Bakhoum, Memorial Sloan Kettering Cancer Center
Pegah Khosravi, CUNY New York City College of TechnologyFollow
Olivier Elemento, Weill Cornell Medical College

Document Type

Article

Publication Date

5-21-2021

Abstract

Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.

Comments

This article was originally published in iScience, available at https://doi.org/10.1016/j.isci.2021.102394

This work is distributed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).