{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:04:29Z","timestamp":1793160269081,"version":"4.5.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T00:00:00Z","timestamp":1712275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Insights Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To evaluate whether the quantitative abnormality scores provided by artificial intelligence (AI)-based computer-aided detection\/diagnosis (CAD) for mammography interpretation can be used to predict invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Four hundred forty DCIS in 420 women (mean age, 52.8 years) diagnosed via percutaneous biopsy from January 2015 to December 2019 were included. Mammographic characteristics were assessed based on imaging features (mammographically occult, mass\/asymmetry\/distortion, calcifications only, and combined mass\/asymmetry\/distortion with calcifications) and BI-RADS assessments. Routine pre-biopsy 4-view digital mammograms were analyzed using AI-CAD to obtain abnormality scores (AI-CAD score, ranging 0\u2013100%). Multivariable logistic regression was performed to identify independent predictive mammographic variables after adjusting for clinicopathological variables. A subgroup analysis was performed with mammographically detected DCIS.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of the 440 DCIS, 117 (26.6%) were upgraded to invasive cancer. Three hundred forty-one (77.5%) DCIS were detected on mammography. The multivariable analysis showed that combined features (odds ratio (OR): 2.225, <jats:italic>p<\/jats:italic> = 0.033), BI-RADS 4c or 5 assessments (OR: 2.473, <jats:italic>p<\/jats:italic> = 0.023 and OR: 5.190, <jats:italic>p<\/jats:italic> &lt; 0.001, respectively), higher AI-CAD score (OR: 1.009, <jats:italic>p<\/jats:italic> = 0.007), AI-CAD score \u2265 50% (OR: 1.960, <jats:italic>p<\/jats:italic> = 0.017), and AI-CAD score \u2265 75% (OR: 2.306, <jats:italic>p<\/jats:italic> = 0.009) were independent predictors of invasive upgrade. In mammographically detected DCIS, combined features (OR: 2.194, <jats:italic>p<\/jats:italic> = 0.035), and higher AI-CAD score (OR: 1.008, <jats:italic>p<\/jats:italic> = 0.047) were significant predictors of invasive upgrade.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The AI-CAD score was an independent predictor of invasive upgrade for DCIS. Higher AI-CAD scores, especially in the highest quartile of \u2265 75%, can be used as an objective imaging biomarker to predict invasive upgrade in DCIS diagnosed with percutaneous biopsy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Critical relevance statement<\/jats:title>\n                <jats:p>Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key points<\/jats:title>\n                <jats:p>\u2022 Predicting ductal carcinoma in situ upgrade is important, yet there is a lack of conclusive non-invasive biomarkers.<\/jats:p>\n                <jats:p>\u2022 AI-CAD scores\u2014raw numbers, \u2265 50%, and \u2265 75%\u2014predicted ductal carcinoma in situ upgrade independently.<\/jats:p>\n                <jats:p>\u2022 Quantitative AI-CAD results may help predict ductal carcinoma in situ upgrade and guide patient management.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s13244-024-01673-0","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T12:02:15Z","timestamp":1712318535000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["AI analytics can be used as imaging biomarkers for predicting invasive upgrade of ductal carcinoma in situ"],"prefix":"10.1186","volume":"15","author":[{"given":"Jiyoung","family":"Yoon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juyeon","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Sun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Jung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivian Youngjean","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miribi","family":"Rho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2100-3513","authenticated-orcid":false,"given":"Jung Hyun","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"1673_CR1","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/modpathol.2014.141","volume":"28","author":"ME Sanders","year":"2015","unstructured":"Sanders ME, Schuyler PA, Simpson JF, Page DL, Dupont WD (2015) Continued observation of the natural history of low-grade ductal carcinoma in situ reaffirms proclivity for local recurrence even after more than 30 years of follow-up. 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