{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T03:16:09Z","timestamp":1772853369638,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Pathology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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 &amp; 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 &amp; E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.<\/jats:p>","DOI":"10.3390\/cancers15071922","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T02:35:26Z","timestamp":1679538926000},"page":"1922","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Soumya","family":"Ghose","sequence":"first","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6715-9348","authenticated-orcid":false,"given":"Sanghee","family":"Cho","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiona","family":"Ginty","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elizabeth","family":"McDonough","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Davis","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanpan","family":"Zhang","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2347-7210","authenticated-orcid":false,"given":"Jhimli","family":"Mitra","sequence":"additional","affiliation":[{"name":"GE Research Center, Niskayuna, NY 12309, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1376-8409","authenticated-orcid":false,"given":"Adrian L.","family":"Harris","sequence":"additional","affiliation":[{"name":"Department of Oncology, Cancer and Haematology Centre, Oxford University, Oxford OX3 9DU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aye Aye","family":"Thike","sequence":"additional","affiliation":[{"name":"Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Puay Hoon","family":"Tan","sequence":"additional","affiliation":[{"name":"Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9927-4893","authenticated-orcid":false,"given":"Yesim","family":"G\u00f6kmen-Polar","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA"},{"name":"Winship Cancer Institute, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8861-9980","authenticated-orcid":false,"given":"Sunil S.","family":"Badve","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA"},{"name":"Winship Cancer Institute, Atlanta, GA 30322, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.pathol.2019.07.005","article-title":"Ductal carcinoma in situ of breast: Update 2019","volume":"51","author":"Badve","year":"2019","journal-title":"Pathology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"bqac027","DOI":"10.1210\/endocr\/bqac027","article-title":"Ductal Carcinoma In Situ of Breast: From Molecular Etiology to Therapeutic Management","volume":"163","author":"Hophan","year":"2022","journal-title":"Endocrinology"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Early Breast Cancer Trialists\u2019 Collaborative Group (2010). 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