{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:09:11Z","timestamp":1785510551998,"version":"3.56.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"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":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1\u2013L4 vertebrae derived from DXA (r\u2009=\u20090.91, p\u2009&lt;\u20090.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R<jats:sup>2<\/jats:sup> value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.<\/jats:p>","DOI":"10.1007\/s13246-023-01360-2","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T12:02:37Z","timestamp":1704715357000},"page":"239-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment"],"prefix":"10.1007","volume":"47","author":[{"given":"Yu-Hsuan","family":"Lai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Shan","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei-Fang","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chung-I","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helen H. W.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"1360_CR1","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/s0140-6736(16)31392-7","volume":"389","author":"O Ginsburg","year":"2017","unstructured":"Ginsburg O, Bray F, Coleman MP et al (2017) The global burden of women\u2019s cancers: a grand challenge in global health. Lancet 389:847\u2013860. https:\/\/doi.org\/10.1016\/s0140-6736(16)31392-7","journal-title":"Lancet"},{"key":"1360_CR2","doi-asserted-by":"publisher","first-page":"1408793","DOI":"10.1155\/2020\/1408793","volume":"2020","author":"YC Huang","year":"2020","unstructured":"Huang YC, Chen YH (2020) Cancer incidence characteristic evolution based on the national cancer registry in Taiwan. J Oncol 2020:1408793. https:\/\/doi.org\/10.1155\/2020\/1408793","journal-title":"J Oncol"},{"key":"1360_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11864-021-00835-2","volume":"22","author":"A Diana","year":"2021","unstructured":"Diana A, Carlino F, Giunta EF et al (2021) Cancer treatment-induced bone loss (CTIBL): state of the art and proper management in breast cancer patients on endocrine therapy. Curr Treat Options Oncol 22:45. https:\/\/doi.org\/10.1007\/s11864-021-00835-2","journal-title":"Curr Treat Options Oncol"},{"key":"1360_CR4","doi-asserted-by":"publisher","first-page":"100355","DOI":"10.1016\/j.jbo.2021.100355","volume":"28","author":"K Waqas","year":"2021","unstructured":"Waqas K, Lima Ferreira J, Tsourdi E, Body JJ, Hadji P, Zillikens MC (2021) Updated guidance on the management of cancer treatment-induced bone loss (CTIBL) in pre- and postmenopausal women with early-stage breast cancer. J Bone Oncol 28:100355. https:\/\/doi.org\/10.1016\/j.jbo.2021.100355","journal-title":"J Bone Oncol"},{"key":"1360_CR5","doi-asserted-by":"publisher","first-page":"100254","DOI":"10.1016\/j.jbo.2019.100254","volume":"18","author":"U Stumpf","year":"2019","unstructured":"Stumpf U, Kostev K, Kyvernitakis J, B\u00f6cker W, Hadji P (2019) Incidence of fractures in young women with breast cancer\u2014a retrospective cohort study. J Bone Oncol 18:100254. https:\/\/doi.org\/10.1016\/j.jbo.2019.100254","journal-title":"J Bone Oncol"},{"key":"1360_CR6","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.1002\/cncr.30682","volume":"123","author":"A Lipton","year":"2017","unstructured":"Lipton A, Chapman JW, Leitzel K et al (2017) Osteoporosis therapy and outcomes for postmenopausal patients with hormone receptor-positive breast cancer: NCIC CTG MA.27. Cancer 123:2444\u20132451. https:\/\/doi.org\/10.1002\/cncr.30682","journal-title":"Cancer"},{"key":"1360_CR7","doi-asserted-by":"publisher","first-page":"783","DOI":"10.6004\/jnccn.2017.0104","volume":"15","author":"B Dull","year":"2017","unstructured":"Dull B, Linkugel A, Margenthaler JA, Cyr AE (2017) Overuse of chest CT in patients with stage I and II breast cancer: an opportunity to increase guidelines compliance at an NCCN member institution. J Natl Compr Cancer Netw JNCCN 15:783\u2013789. https:\/\/doi.org\/10.6004\/jnccn.2017.0104","journal-title":"J Natl Compr Cancer Netw JNCCN"},{"key":"1360_CR8","doi-asserted-by":"publisher","first-page":"e0240084","DOI":"10.1371\/journal.pone.0240084","volume":"15","author":"SH Park","year":"2020","unstructured":"Park SH, Jeong YM, Lee HY et al (2020) Opportunistic use of chest CT for screening osteoporosis and predicting the risk of incidental fracture in breast cancer patients: a retrospective longitudinal study. PLoS ONE 15:e0240084. https:\/\/doi.org\/10.1371\/journal.pone.0240084","journal-title":"PLoS ONE"},{"key":"1360_CR9","doi-asserted-by":"publisher","first-page":"4042","DOI":"10.1200\/jco.2003.08.017","volume":"21","author":"BE Hillner","year":"2003","unstructured":"Hillner BE, Ingle JN, Chlebowski RT et al (2003) American Society of Clinical Oncology 2003 update on the role of bisphosphonates and bone health issues in women with breast cancer. J Clin Oncol 21:4042\u20134057. https:\/\/doi.org\/10.1200\/jco.2003.08.017","journal-title":"J Clin Oncol"},{"issue":"3","key":"1360_CR10","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.6004\/jnccn.2013.0215","volume":"11","author":"JR Gralow","year":"2013","unstructured":"Gralow JR, Biermann JS, Farooki A et al (2013) NCCN task force report: bone health in cancer care. J Natl Compr Cancer Netw JNCCN 11(3):S1\u2013S50. https:\/\/doi.org\/10.6004\/jnccn.2013.0215","journal-title":"J Natl Compr Cancer Netw JNCCN"},{"key":"1360_CR11","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s00198-016-3804-3","volume":"28","author":"E Alacreu","year":"2017","unstructured":"Alacreu E, Moratal D, Arana E (2017) Opportunistic screening for osteoporosis by routine CT in Southern Europe. Osteoporos Int 28:983\u2013990. https:\/\/doi.org\/10.1007\/s00198-016-3804-3","journal-title":"Osteoporos Int"},{"key":"1360_CR12","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1148\/radiol.2019181648","volume":"291","author":"S Jang","year":"2019","unstructured":"Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ (2019) Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology 291:360\u2013367. https:\/\/doi.org\/10.1148\/radiol.2019181648","journal-title":"Radiology"},{"key":"1360_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441\u2013446. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036","journal-title":"Eur J Cancer"},{"key":"1360_CR14","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.7150\/thno.30309","volume":"9","author":"Z Liu","year":"2019","unstructured":"Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303\u20131322. https:\/\/doi.org\/10.7150\/thno.30309","journal-title":"Theranostics"},{"key":"1360_CR15","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.diii.2020.01.008","volume":"101","author":"S Rastegar","year":"2020","unstructured":"Rastegar S, Vaziri M, Qasempour Y et al (2020) Radiomics for classification of bone mineral loss: a machine learning study. Diagn Interv Imaging 101:599\u2013610. https:\/\/doi.org\/10.1016\/j.diii.2020.01.008","journal-title":"Diagn Interv Imaging"},{"key":"1360_CR16","first-page":"1","volume":"843","author":"World Health Organization","year":"1994","unstructured":"World Health Organization (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group. World Health Organ Tech Rep Ser 843:1\u2013129","journal-title":"World Health Organ Tech Rep Ser"},{"key":"1360_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14081895","author":"YC Huang","year":"2022","unstructured":"Huang YC, Tsai YS, Li CI et al (2022) Adjusted CT image-based radiomic features combined with immune genomic expression achieve accurate prognostic classification and identification of therapeutic targets in stage III colorectal cancer. Cancers. https:\/\/doi.org\/10.3390\/cancers14081895","journal-title":"Cancers"},{"key":"1360_CR18","doi-asserted-by":"publisher","first-page":"3549","DOI":"10.1007\/s00330-020-06677-0","volume":"30","author":"K Yasaka","year":"2020","unstructured":"Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O (2020) Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol 30:3549\u20133557. https:\/\/doi.org\/10.1007\/s00330-020-06677-0","journal-title":"Eur Radiol"},{"key":"1360_CR19","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.Can-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104\u2013e107. https:\/\/doi.org\/10.1158\/0008-5472.Can-17-0339","journal-title":"Cancer Res"},{"key":"1360_CR20","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155\u2013163. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J Chiropr Med"},{"key":"1360_CR21","doi-asserted-by":"publisher","first-page":"10545","DOI":"10.1038\/s41598-018-28895-9","volume":"8","author":"M Shafiq-ul-Hassan","year":"2018","unstructured":"Shafiq-ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8:10545. https:\/\/doi.org\/10.1038\/s41598-018-28895-9","journal-title":"Sci Rep"},{"key":"1360_CR22","doi-asserted-by":"publisher","first-page":"35","DOI":"10.2152\/jmi.66.35","volume":"66","author":"A Haga","year":"2019","unstructured":"Haga A, Takahashi W, Aoki S et al (2019) Standardization of imaging features for radiomics analysis. J Med Invest 66:35\u201337. https:\/\/doi.org\/10.2152\/jmi.66.35","journal-title":"J Med Invest"},{"key":"1360_CR23","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1002\/bimj.201300204","volume":"56","author":"K Langohr","year":"2014","unstructured":"Langohr K, Melis GG (2014) Estimation and residual analysis with R for a linear regression model with an interval-censored covariate. Biometrical J 56:867\u2013885. https:\/\/doi.org\/10.1002\/bimj.201300204","journal-title":"Biometrical J"},{"key":"1360_CR24","doi-asserted-by":"publisher","first-page":"588","DOI":"10.7326\/0003-4819-158-8-201304160-00003","volume":"158","author":"PJ Pickhardt","year":"2013","unstructured":"Pickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N (2013) Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158:588\u2013595. https:\/\/doi.org\/10.7326\/0003-4819-158-8-201304160-00003","journal-title":"Ann Intern Med"},{"key":"1360_CR25","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1016\/j.spinee.2021.03.008","volume":"21","author":"DP Ahern","year":"2021","unstructured":"Ahern DP, McDonnell JM, Riffault M et al (2021) A meta-analysis of the diagnostic accuracy of Hounsfield units on computed topography relative to dual-energy X-ray absorptiometry for the diagnosis of osteoporosis in the spine surgery population. Spine J 21:1738\u20131749. https:\/\/doi.org\/10.1016\/j.spinee.2021.03.008","journal-title":"Spine J"},{"key":"1360_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08805-4","author":"YW Jiang","year":"2022","unstructured":"Jiang YW, Xu XJ, Wang R, Chen CM (2022) Radiomics analysis based on lumbar spine CT to detect osteoporosis. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-022-08805-4","journal-title":"Eur Radiol"},{"key":"1360_CR27","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1200\/jco.2013.54.2258","volume":"32","author":"HJ Burstein","year":"2014","unstructured":"Burstein HJ, Temin S, Anderson H et al (2014) Adjuvant endocrine therapy for women with hormone receptor-positive breast cancer: american society of clinical oncology clinical practice guideline focused update. J Clin Oncol 32:2255\u20132269. https:\/\/doi.org\/10.1200\/jco.2013.54.2258","journal-title":"J Clin Oncol"},{"key":"1360_CR28","first-page":"562","volume":"32","author":"KJ Jerzak","year":"2018","unstructured":"Jerzak KJ, Raphael J, Desautels DN, Blanchette PS, Tyono I, Pritchard KI (2018) Bone-targeted therapy in early breast cancer. Oncology 32:562\u2013569","journal-title":"Oncology"},{"key":"1360_CR29","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1200\/jco.2007.11.0726","volume":"26","author":"R Eastell","year":"2008","unstructured":"Eastell R, Adams JE, Coleman RE et al (2008) Effect of anastrozole on bone mineral density: 5-year results from the anastrozole, tamoxifen, alone or in combination trial 18233230. J Clin Oncol 26:1051\u20131057. https:\/\/doi.org\/10.1200\/jco.2007.11.0726","journal-title":"J Clin Oncol"},{"key":"1360_CR30","doi-asserted-by":"publisher","first-page":"e165","DOI":"10.1016\/j.acra.2020.03.046","volume":"28","author":"L He","year":"2021","unstructured":"He L, Liu Z, Liu C et al (2021) Radiomics based on lumbar spine magnetic resonance imaging to detect osteoporosis. Acad Radiol 28:e165\u2013e171. https:\/\/doi.org\/10.1016\/j.acra.2020.03.046","journal-title":"Acad Radiol"},{"key":"1360_CR31","doi-asserted-by":"publisher","DOI":"10.1177\/02841851211068149","author":"H Dai","year":"2021","unstructured":"Dai H, Wang Y, Fu R et al (2021) Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiol. https:\/\/doi.org\/10.1177\/02841851211068149","journal-title":"Acta Radiol"},{"key":"1360_CR32","doi-asserted-by":"publisher","first-page":"776","DOI":"10.3238\/arztebl.2010.0776","volume":"107","author":"A Schneider","year":"2010","unstructured":"Schneider A, Hommel G, Blettner M (2010) Linear regression analysis: part 14 of a series on evaluation of scientific publications. Dtsch Arztebl Int 107:776\u2013782. https:\/\/doi.org\/10.3238\/arztebl.2010.0776","journal-title":"Dtsch Arztebl Int"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01360-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-023-01360-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01360-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T10:10:16Z","timestamp":1711361416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-023-01360-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["1360"],"URL":"https:\/\/doi.org\/10.1007\/s13246-023-01360-2","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"21 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no commercial affiliation, conflict of interest or any potential financial interests involved in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This retrospective study was approved by the institutional review board of the National Cheng Kung University Hospital (NCKUH).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}