{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:59:47Z","timestamp":1789174787794,"version":"build-2803163510"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The main objective of this study is to assess the possibility of using radiomics, deep learning, and transfer learning methods for the analysis of chest CT scans. An additional aim is to combine these techniques with bone turnover markers to identify and screen for osteoporosis in patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>A total of 488 patients who had undergone chest CT and bone turnover marker testing, and had known bone mineral density, were included in this study. ITK-SNAP software was used to delineate regions of interest, while radiomics features were extracted using Python. Multiple 2D and 3D deep learning models were trained to identify these regions of interest. The effectiveness of these techniques in screening for osteoporosis in patients was compared.<\/jats:p><\/jats:sec><jats:sec><jats:title>Result<\/jats:title><jats:p>Clinical models based on gender, age, and \u03b2-cross achieved an accuracy of 0.698 and an AUC of 0.665. Radiomics models, which utilized 14 selected radiomics features, achieved a maximum accuracy of 0.750 and an AUC of 0.739. The test group yielded promising results: the 2D Deep Learning model achieved an accuracy of 0.812 and an AUC of 0.855, while the 3D Deep Learning model performed even better with an accuracy of 0.854 and an AUC of 0.906. Similarly, the 2D Transfer Learning model achieved an accuracy of 0.854 and an AUC of 0.880, whereas the 3D Transfer Learning model exhibited an accuracy of 0.740 and an AUC of 0.737. Overall, the application of 3D deep learning and 2D transfer learning techniques on chest CT scans showed excellent screening performance in the context of osteoporosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Bone turnover markers may not be necessary for osteoporosis screening, as 3D deep learning and 2D transfer learning techniques utilizing chest CT scans proved to be equally effective alternatives.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2024.1296047","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T05:35:39Z","timestamp":1717479339000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["A comprehensive approach for osteoporosis detection through chest CT analysis and bone turnover markers: harnessing radiomics and deep learning techniques"],"prefix":"10.3389","volume":"15","author":[{"given":"Kaibin","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaocong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangsheng","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.clinbiochem.2022.09.002","article-title":"Current use of bone turnover markers in the management of osteoporosis","author":"Brown","year":"2022","journal-title":"Clin Biochem"},{"key":"B2","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00198-010-1501-1","article-title":"Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards","volume":"22","author":"Vasikaran","year":"2011","journal-title":"Osteoporos Int"},{"key":"B3","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1016\/j.ajog.2005.08.047","article-title":"Epidemiology, etiology, and diagnosis of osteoporosis","volume":"194","author":"Lane","year":"2006","journal-title":"Am J Obstet Gynecol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-8587(17)30184-5","article-title":"Use of bone turnover markers in postmenopausal osteoporosis","volume":"5","author":"Eastell","year":"2017","journal-title":"Lancet Diabetes Endocrinol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"E5","DOI":"10.3171\/2020.5.FOCUS20241","article-title":"Prevalence of osteoporosis and osteopenia diagnosed using quantitative CT in 296 consecutive lumbar fusion patients","volume":"49","author":"Carlson","year":"2020","journal-title":"Neurosurg Focus"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1097\/GRF.0b013e3182a99814","article-title":"Screening for osteoporosis","volume":"56","author":"Helmrich","year":"2013","journal-title":"Clin Obstet Gynecol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.3390\/ijerph19042460","article-title":"Lung cancer imaging: screening result and nodule management","volume":"19","author":"Guerrini","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J Nucl Med"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr","article-title":"Radiomics and artificial intelligence in lung cancer screening","volume":"10","author":"Binczyk","year":"2021","journal-title":"Transl Lung Cancer Res"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2013022-08805\u20134","article-title":"Radiomics analysis based on lumbar spine CT to detect osteoporosis","volume":"32","author":"Jiang","year":"2022","journal-title":"Eur Radiol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa237","article-title":"Deep learning in systems medicine","volume":"22","author":"Wang","year":"2021","journal-title":"Brief Bioinform"},{"key":"B12","doi-asserted-by":"publisher","first-page":"115561","DOI":"10.1016\/j.bone.2020.115561","article-title":"Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study","volume":"140","author":"Zhang","year":"2020","journal-title":"Bone"},{"key":"B13","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12880-022-00793-7","article-title":"Transfer learning for medical image classification: a literature review","volume":"22","author":"Kim","year":"2022","journal-title":"BMC Med Imaging"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.18","article-title":"KNN matting","volume":"35","author":"Chen","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.4037\/ajcc2018525","article-title":"Predicting pressure injury in critical care patients: A machine-learning model","volume":"27","author":"Alderden","year":"2018","journal-title":"Am J Crit Care"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.apmr.2020.08.011","article-title":"Using machine learning to predict rehabilitation outcomes in postacute hip fracture patients","volume":"102","author":"Shtar","year":"2021","journal-title":"Arch Phys Med Rehabil"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1858300","DOI":"10.1155\/2022\/1858300","article-title":"XGBoost-based E-commerce customer loss prediction","volume":"2022","author":"Gan","year":"2022","journal-title":"Comput Intell Neurosci"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1080\/01443615.2021.1945006","article-title":"LightGBM: an efficient and accurate method for predicting pregnancy diseases","volume":"42","author":"Liao","year":"2022","journal-title":"J Obstet Gynaecol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"241","DOI":"10.21037\/atm","article-title":"Na\u00efve bayes classification in R","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann Transl Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.1c03214","article-title":"AdaBoost metalearning methodology for modeling the incipient dissociation conditions of clathrate hydrates","volume":"6","author":"Keshvari","year":"2021","journal-title":"ACS Omega"},{"key":"B21","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1038\/s41598-020-59115-y","article-title":"Machine learning algorithms for predicting the recurrence of stage IV colorectal cancer after tumor resection","volume":"10","author":"Xu","year":"2020","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J Clin Epidemiol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12600","article-title":"A parallel MR imaging method using multilayer perceptron","volume":"44","author":"Kwon","year":"2017","journal-title":"Med Phys"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1097\/BOT.0000000000000949","article-title":"Osteoporotic thoracolumbar fractures-how are they different?-classification and treatment algorithm","author":"Rajasekaran","year":"2017","journal-title":"J Orthop Trauma"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"B26","first-page":"1","article-title":"Machine learning","author":"Zhihua","year":"2018","journal-title":"Airport"},{"key":"B27","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1515\/ijb-2020-0091","article-title":"The area under the generalized receiver-operating characteristic curve","volume":"18","author":"Mart\u00ednez-Camblor","year":"2021","journal-title":"Int J Biostat"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1007\/s10985\u2013022-09571\u20137","article-title":"Assessing dynamic covariate effects with survival data","volume":"28","author":"Cui","year":"2022","journal-title":"Lifetime Data Anal"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.2466\/pr0.94.3c.1419-1420","article-title":"Wilcoxon test after Levene's transformation can have an inflated Type I error rate","volume":"94","author":"Neuh\u00e4user","year":"2004","journal-title":"Psychol Rep"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5328","article-title":"Misuse of DeLong test to compare AUCs for nested models","volume":"31","author":"Demler","year":"2012","journal-title":"Stat Med"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1111\/j.1526-4637.2006.00140.x","article-title":"Osteoporotic fractures: mortality and quality of life","volume":"49","author":"Caliri","year":"2007","journal-title":"Panminerva Med"},{"key":"B32","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/S0140-6736(22)01694-4","article-title":"Lung cancer screening","volume":"401","author":"Adams","year":"2023","journal-title":"Lancet"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.chest.2022.08.2207","article-title":"The introduction of low-dose CT imaging and lung cancer overdiagnosis in chinese women","volume":"163","author":"Wang","year":"2023","journal-title":"Chest"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-021-05900-y","article-title":"The clinician's guide to prevention and treatment of osteoporosis","volume":"33","author":"LeBoff","year":"2022","journal-title":"Osteoporos Int"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.2147\/CIA","article-title":"The epidemiology and management of postmenopausal osteoporosis: a viewpoint from Brazil","volume":"10","author":"Baccaro","year":"2015","journal-title":"Clin Interv Aging"},{"key":"B36","doi-asserted-by":"publisher","first-page":"CD001991","DOI":"10.1002\/14651858","article-title":"Screening for lung cancer","volume":"2013","author":"Manser","year":"2013","journal-title":"Cochrane Database Syst Rev"},{"key":"B37","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1186\/s12891-020-03217-1","article-title":"Awareness of osteoporosis among 368 residents in China: a cross-sectional study","volume":"21","author":"Oumer","year":"2020","journal-title":"BMC Musculoskelet Disord"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)61647-5","article-title":"Effects of vitamin D supplements on bone mineral density: a systematic review and meta-analysis","volume":"383","author":"Reid","year":"2014","journal-title":"Lancet"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1007\/s11154-017-9424-1","article-title":"The vitamin D deficiency pandemic: Approaches for diagnosis, treatment and prevention","volume":"18","author":"Holick","year":"2017","journal-title":"Rev Endocr Metab Disord"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-014-2646-0","article-title":"PINP as a biological response marker during teriparatide treatment for osteoporosis","volume":"25","author":"Krege","year":"2014","journal-title":"Osteoporos Int"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.21037\/apm","article-title":"Application of bone turnover markers and DXA and QCT in an elderly Chinese male population","volume":"10","author":"Yuan","year":"2021","journal-title":"Ann Palliat Med"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1007\/s00223-021-00930-4","article-title":"IOF-IFCC joint committee on bone metabolism (C-BM). Practical considerations for the clinical application of bone turnover markers in osteoporosis","volume":"112","author":"Vasikaran","year":"2023","journal-title":"Calcif Tissue Int"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1111\/os.12596","article-title":"Bone turnover markers and bone mineral density to predict osteoporotic fractures in older women: A retrospective comparative study","volume":"12","author":"Qu","year":"2020","journal-title":"Orthop Surg"},{"key":"B44","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s12891-022-06096-w","article-title":"Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT","volume":"24","author":"Wang","year":"2023","journal-title":"BMC Musculoskelet Disord"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-022-06491-y","article-title":"Opportunistic osteoporosis screening using chest CT with artificial intelligence","volume":"33","author":"Yang","year":"2022","journal-title":"Osteoporos Int"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1002\/jbmr.4814","article-title":"Deep-learning-based detection of vertebral fracture and osteoporosis using lateral spine X-ray radiography","volume":"38","author":"Hong","year":"2023","journal-title":"J Bone Miner Res"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibneur.2022.08.010","article-title":"Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer's disease","volume":"13","author":"Nguyen","year":"2022","journal-title":"IBRO Neurosci Rep"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2024.1296047\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T10:06:51Z","timestamp":1721383611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2024.1296047\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,4]]},"references-count":47,"alternative-id":["10.3389\/fendo.2024.1296047"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2024.1296047","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,4]]},"article-number":"1296047"}}