End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer

Seyed Mostafa M. Kahaki; Ian S. Hagemann; Kenny H. Cha; Christopher J. Trindade; Nicholas A. Petrick; Nicolas Kostelecky; Lindsay E. Borden; Doaa Atwi; Kar-Ming Fung; Weijie Chen

doi:10.1117/1.JMI.11.1.017502

15 February 2024 End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer

Seyed Mostafa M. Kahaki, Ian S. Hagemann, Kenny H. Cha, Christopher J. Trindade, Nicholas A. Petrick, Nicolas Kostelecky, Lindsay E. Borden, Doaa Atwi, Kar-Ming Fung, Weijie Chen

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 1, 017502 (February 2024). https://doi.org/10.1117/1.JMI.11.1.017502

Abstract

Purpose

Endometrial cancer (EC) is the most common gynecologic malignancy in the United States, and atypical endometrial hyperplasia (AEH) is considered a high-risk precursor to EC. Hormone therapies and hysterectomy are practical treatment options for AEH and early-stage EC. Some patients prefer hormone therapies for reasons such as fertility preservation or being poor surgical candidates. However, accurate prediction of an individual patient’s response to hormonal treatment would allow for personalized and potentially improved recommendations for these conditions. This study aims to explore the feasibility of using deep learning models on whole slide images (WSI) of endometrial tissue samples to predict the patient’s response to hormonal treatment.

Approach

We curated a clinical WSI dataset of 112 patients from two clinical sites. An expert pathologist annotated these images by outlining AEH/EC regions. We developed an end-to-end machine learning model with mixed supervision. The model is based on image patches extracted from pathologist-annotated AEH/EC regions. Either an unsupervised deep learning architecture (Autoencoder or ResNet50), or non-deep learning (radiomics feature extraction) is used to embed the images into a low-dimensional space, followed by fully connected layers for binary prediction, which was trained with binary responder/non-responder labels established by pathologists. We used stratified sampling to partition the dataset into a development set and a test set for internal validation of the performance of our models.

Results

The autoencoder model yielded an AUROC of 0.80 with 95% CI [0.63, 0.95] on the independent test set for the task of predicting a patient with AEH/EC as a responder vs non-responder to hormonal treatment.

Conclusions

These findings demonstrate the potential of using mixed supervised machine learning models on WSIs for predicting the response to hormonal treatment in AEH/EC patients.

Citation Download Citation

Seyed Mostafa M. Kahaki, Ian S. Hagemann, Kenny H. Cha, Christopher J. Trindade, Nicholas A. Petrick, Nicolas Kostelecky, Lindsay E. Borden, Doaa Atwi, Kar-Ming Fung, and Weijie Chen "End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer," Journal of Medical Imaging 11(1), 017502 (15 February 2024). https://doi.org/10.1117/1.JMI.11.1.017502

Received: 1 June 2023; Accepted: 16 January 2024; Published: 15 February 2024

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KEYWORDS

Data modeling

Deep learning

Education and training

Feature extraction

Performance modeling

Machine learning

Radiomics

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