Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study

Seyed Kahaki; Ian S. Hagemann; Kenny Cha; Christopher J. Trindade; Nicholas Petrick; Nicolas Kostelecky; Weijie Chen

doi:10.1117/12.2652912

6 April 2023 Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study

Seyed Kahaki, Ian S. Hagemann, Kenny Cha, Christopher J. Trindade, Nicholas Petrick, Nicolas Kostelecky, Weijie Chen

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710T (2023) https://doi.org/10.1117/12.2652912
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Endometrial cancer (EC) is the most common gynecologic malignancy in the US and complex atypical hyperplasia (CAH) is considered a high-risk precursor to EC. Treatment options for CAH and early-stage EC include hormone therapies and hysterectomy with the former preferred by certain patients, e.g., for fertility preservation or poor surgical candidates. Accurate prediction of response to hormonal treatment would allow for personalized and potentially improved recommendations for the treatment of these conditions. In this study, we investigate the feasibility of utilizing weakly supervised deep learning models on whole slide images of endometrial tissue samples for the prediction of patient response to hormonal treatment. We curated a clinical whole-slide-image (WSI) dataset of 112 patients from two clinical sites. We developed an end-to-end machine learning model using WSIs of endometrial specimens for the prediction of hormonal treatment response among women with CAH/EC. The model takes patches extracted from pathologist-annotated CAH/EC regions as input and utilizes an unsupervised deep learning architecture (Autoencoder or ResNet50) to embed the images into a low-dimensional space, followed by fully connected layers for binary prediction. Our autoencoder model yielded an AUC of 0.79 with 95% CI [0.61, 0.98] on a hold-out test set in the task of predicting a patient with CAH/EC as a responder vs non-responder to hormonal treatment. Our results, demonstrate the potential for using weakly supervised machine learning models on WSIs for predicting response to hormonal treatment of CAH/EC patients.

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Seyed Kahaki, Ian S. Hagemann, Kenny Cha, Christopher J. Trindade, Nicholas Petrick, Nicolas Kostelecky, and Weijie Chen "Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710T (6 April 2023); https://doi.org/10.1117/12.2652912

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