{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T01:42:13Z","timestamp":1785894133016,"version":"3.56.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008375","name":"University of Basel","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008375","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In total, 4605 synthetic 2D images (1665 patients, age: 57\u2009\u00b1\u200937\u00a0years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11\u00a0years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen\u2019s kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2\u201386.9), a specificity of 89.3% (95%-CI 85.4\u201392.3), and an accuracy of 89.6% (95%-CI 88.1\u201390.9) in the differentiation between ACR A\/B and ACR C\/D. DCNN versus human and inter-reader agreement were both \u201csubstantial\u201d (Cohen\u2019s kappa: 0.61 versus 0.63).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions<\/jats:italic>.<\/jats:p>\n                <jats:p>\u2022 <jats:italic>The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis<\/jats:italic>.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-023-09474-7","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T06:04:01Z","timestamp":1677650641000},"page":"4589-4596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-7912","authenticated-orcid":false,"given":"Raphael","family":"Sexauer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patryk","family":"Hejduk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karol","family":"Borkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlotta","family":"Ruppert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Weikert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Dellas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noemi","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"9474_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.2147\/BCTT.S39114","volume":"6","author":"P Advani","year":"2014","unstructured":"Advani P, Moreno-Aspitia A (2014) Current strategies for the prevention of breast cancer. 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