Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models

Cell Rep Med. 2021 Sep 23;2(9):100400. doi: 10.1016/j.xcrm.2021.100400. eCollection 2021 Sep 21.

Abstract

The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.

Keywords: cancer genomics; cancer imaging; computational biology; computational pathology; deep learning; endometrial carcinoma.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Area Under Curve
  • Deep Learning
  • Endometrial Neoplasms / classification*
  • Endometrial Neoplasms / diagnosis*
  • Endometrial Neoplasms / genetics
  • Endometrial Neoplasms / pathology
  • Female
  • Humans
  • Imaging, Three-Dimensional*
  • ROC Curve