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Automatic assessment of spinal bone measures in CT included segmentation of vertebrae using a convolutional neural network (CNN), reduction to the vertebral body, and extraction of bone mineral content (BMC), trabecular and integral volumetric bone mineral density (vBMD), and CT-based areal BMD (aBMD) using asynchronous calibration. Moreover, trabecular bone was manually sampled (manual vBMD).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A total of 148 patients (77%) had vertebral fractures and significantly lower values in all bone measures compared to patients without fractures (<jats:italic>p<\/jats:italic> \u2264 0.001). Except for BMC, all CT-based measures performed significantly better as predictors for vertebral fractures compared to DXA (e.g., AUC = 0.885 for trabecular vBMD and AUC = 0.86 for integral vBMD vs. AUC = 0.668 for DXA aBMD, respectively; both <jats:italic>p<\/jats:italic> &lt; 0.001). Age- and sex-adjusted associations with fracture status were strongest for manual vBMD (OR = 7.3, [95%] CI 3.8\u201314.3) followed by automatically assessed trabecular vBMD (OR = 6.9, CI 3.5\u201313.4) and integral vBMD (OR = 4.3, CI 2.5\u20137.6). Diagnostic cutoffs of integral vBMD for osteoporosis (&lt; 160 mg\/cm<jats:sup>3<\/jats:sup>) or low bone mass (160 \u2264 BMD &lt; 190 mg\/cm<jats:sup>3<\/jats:sup>) had sensitivity (84%\/41%) and specificity (78%\/95%) similar to trabecular vBMD.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Fully automatic osteoporosis screening in routine CT of the spine is feasible. CT-based measures can better identify individuals with reduced bone mass who suffered from vertebral fractures than DXA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>Opportunistic osteoporosis screening of spinal bone measures derived from clinical routine CT is feasible in a fully automatic fashion using a deep learning-driven framework (<\/jats:italic><jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/anduin.bonescreen.de\"><jats:italic>https:\/\/anduin.bonescreen.de<\/jats:italic><\/jats:ext-link><jats:italic>).<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Manually sampled volumetric BMD (vBMD) and automatically assessed trabecular and integral vBMD were the best predictors for prevalent vertebral fractures.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Except for bone mineral content, all CT-based bone measures performed significantly better than DXA-based measures.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>We introduce diagnostic thresholds of integral vBMD for osteoporosis (&lt; 160 mg\/cm<\/jats:italic><jats:sup><jats:italic>3<\/jats:italic><\/jats:sup><jats:italic>) and low bone mass (160 \u2264 BMD &lt; 190 mg\/cm<\/jats:italic><jats:sup><jats:italic>3<\/jats:italic><\/jats:sup><jats:italic>) with almost equal sensitivity and specificity compared to conventional thresholds of quantitative CT as proposed by the American College of Radiology (osteoporosis &lt; 80 mg\/cm<\/jats:italic><jats:sup><jats:italic>3<\/jats:italic><\/jats:sup><jats:italic>).<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-020-07655-2","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T12:31:02Z","timestamp":1611837062000},"page":"6069-6077","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6022-3682","authenticated-orcid":false,"given":"Maximilian T.","family":"L\u00f6ffler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Jacob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Scharr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Sollmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Egon","family":"Burian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malek","family":"El Husseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjany","family":"Sekuboyina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giles","family":"Tetteh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claus","family":"Zimmer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Gempt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Baum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan S.","family":"Kirschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"7655_CR1","doi-asserted-by":"crossref","unstructured":"NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy (2001) Osteoporosis prevention, diagnosis, and therapy. 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Front Endocrinol 11. https:\/\/doi.org\/10.3389\/fendo.2020.552719","DOI":"10.3389\/fendo.2020.552719"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-020-07655-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-020-07655-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-020-07655-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T11:22:03Z","timestamp":1625829723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-020-07655-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,28]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["7655"],"URL":"https:\/\/doi.org\/10.1007\/s00330-020-07655-2","relation":{},"ISSN":["0938-7994","1432-1084"],"issn-type":[{"value":"0938-7994","type":"print"},{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,28]]},"assertion":[{"value":"8 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Jan S. Kirschke.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Thirty-three patients were included in a prior study comparing manual BMD measurements in CT with DXA in the prediction of incident vertebral fractures [L\u00f6ffler et al 2019]. Moreover, CT scans of 29 patients were previously published as part of a vertebral segmentation dataset with fracture grading [L\u00f6ffler et al 2020].","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study overlap or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 diagnostic or prognostic study\u2022 performed at one institution","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}