{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:14:04Z","timestamp":1787030044099,"version":"build-2736575974"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.<\/jats:p>","DOI":"10.3390\/tomography9010001","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T03:31:06Z","timestamp":1671593466000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0899-1617","authenticated-orcid":false,"given":"Luu-Ngoc","family":"Do","sequence":"first","affiliation":[{"name":"Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7770-6800","authenticated-orcid":false,"given":"Hyo-Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9045-8581","authenticated-orcid":false,"given":"Chaeyeong","family":"Im","sequence":"additional","affiliation":[{"name":"Department of Medicine, Chonnam National University, Gwangju 61469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Hyeok","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Medicine, Chonnam National University, Gwangju 61469, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyo Soon","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea"},{"name":"Department of Radiology, Chonnam National University Hwasun Hospital, Gwangju 58128, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilwoo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea"},{"name":"Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea"},{"name":"Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea"},{"name":"Department of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1093\/jnci\/94.20.1546","article-title":"Detection of ductal carcinoma in situ in women undergoing screening mammography","volume":"94","author":"Ernster","year":"2002","journal-title":"J. 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