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

Abstract

Summary 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.

Deep Learning and Its Applications in Computational Pathology

“…Deep learning architectures that could take advantage of these characteristics are very likely to achieve better results, unveiling more interesting hidden features in histopathology image classification tasks. For instance, a multi-resolution CNN model, which takes advantage of the data structure of .svs and .scn image files, achieves higher performance in classifying endometrial cancer molecular features than its single resolution counterparts [65]. Weakly supervised techniques, such as multiple instance learning, also demonstrate decent performance in classification tasks of histopathology images, and have gained popularity in recent years [64,66,67].…”

Deep Learning and Its Applications in Computational Pathology

“…Deep learning architectures that could take advantage of these characteristics are very likely to get better results and unveiling more interesting hidden features in histopathology image classification tasks. For instance, a multi-resolution CNN model, which takes advantage of the data structure of SVS and SCN image files, achieves higher performance in classifying endometrial cancer molecular features than its single resolution counterparts [64]. Weakly supervised techniques, such as multiple instance learning, also demonstrate decent performance in classification tasks of histopathology images and are gaining popularity in recent years [63,65,66].…”

Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

“…In recent years, deep-learning-based methods have been proved to be able to capture morphological features on tumor images that are associated with molecular features such as mutations, subtypes, and immune infiltration. For example, a customized multi-resolution CNN model showed its power in classifying molecular subtypes in endometrial cancer [6]. An InceptionV3-based model was able to identify BRAF mutations in malignant melanoma tissue [7].…”