{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:18:03Z","timestamp":1789028283284,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T00:00:00Z","timestamp":1652572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Higher Education Sprout Project by the Ministry of Education (MOE)","award":["DP2-109-21121-01-A-03"],"award-info":[{"award-number":["DP2-109-21121-01-A-03"]}]},{"name":"the Higher Education Sprout Project by the Ministry of Education (MOE)","award":["DP2-110-21121-01-A-03"],"award-info":[{"award-number":["DP2-110-21121-01-A-03"]}]},{"name":"the Higher Education Sprout Project by the Ministry of Education (MOE)","award":["DP2-111-21121-01-A-11"],"award-info":[{"award-number":["DP2-111-21121-01-A-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Purpose: Given that early identification of breast cancer type allows for less-invasive therapies, we aimed to develop a machine learning model to discriminate between ductal carcinoma in situ (DCIS) and minimally invasive breast cancer (MIBC). Methods: In this retrospective study, the health records of 420 women who underwent biopsies between 2010 and 2020 to confirm breast cancer were collected. A trained XGBoost algorithm was used to classify cancers as either DCIS or MIBC using clinical characteristics, mammographic findings, ultrasonographic findings, and histopathological features. Its performance was measured against other methods using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, and F1 score. Results: The model was trained using 357 women and tested using 63 women with an overall 420 patients (mean [standard deviation] age, 57.1 [12.0] years). The model performed well when feature importance was determined, reaching an accuracy of 0.84 (95% confidence interval [CI], 0.76\u20130.91), an AUC of 0.93 (95% CI, 0.87\u20130.95), a specificity of 0.75 (95% CI, 0.67\u20130.83), and a sensitivity of 0.91 (95% CI, 0.76\u20130.94). Conclusion: The XGBoost model, combining clinical, mammographic, ultrasonographic, and histopathologic findings, can be used to discriminate DCIS from MIBC with an accuracy equivalent to that of experienced radiologists, thereby giving patients the widest range of therapeutic options.<\/jats:p>","DOI":"10.3390\/cancers14102437","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Machine Learning Algorithm for Distinguishing Ductal Carcinoma In Situ from Invasive Breast Cancer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1832-7998","authenticated-orcid":false,"given":"Vu Pham Thao","family":"Vy","sequence":"first","affiliation":[{"name":"International Master Program of Medicine, Taipei Medical University, Taipei 110, Taiwan"},{"name":"Department of Radiology, Thai Nguyen National Hospital, Thai Nguyen 24000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1295-4771","authenticated-orcid":false,"given":"Melissa Min-Szu","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 110, Taiwan"},{"name":"Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-7926","authenticated-orcid":false,"given":"Nguyen Quoc","family":"Khanh Le","sequence":"additional","affiliation":[{"name":"Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan"},{"name":"Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8322-6336","authenticated-orcid":false,"given":"Wing P.","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 110, Taiwan"},{"name":"Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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