Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

• Type of Material: Article
• Authors: Yesim Polar, Emory University; Soumya Ghose, GE Research Center; Sanghee Cho, GE Research Center; Fiona Ginty, GE Research Center; Elizabeth McDonough, GE Research Center; Cynthia Davis, GE Research Center; Zhanpan Zhang, GE Research Center; Jhimli Mitra, GE Research Center; Adrian L Harris, University of Oxford; Aye Aye Thike, Singapore General Hospital; Pauy Hoon Tan, Singapore General Hospital; Yesim Gökmen-Polar, Emory University; Sunil Badve, Emory University
• Language: English
• Date: 2023-04-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers15071922
• Title of Journal or Parent Work: CANCERS
• Volume: 15
• Issue: 7
• Grant/Funding Information: Department of Pathology funds to support Y.G.-P. and S.S.B.
• Keywords: RISK; LOCAL RECURRENCE; DCIS; SURGERY; OVERDIAGNOSIS; risk-stratification; OVERTREATMENT; breast cancer events; Science & Technology; Oncology; Life Sciences & Biomedicine; deep learning
• Research Categories: Health Sciences, Oncology; Health Sciences, Pathology; Health Sciences, Medicine and Surgery

Abstract

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.