Semin Musculoskelet Radiol 2022; 26(04): 491-500
DOI: 10.1055/s-0042-1754341
Review Article

Imaging of Metabolic Bone Diseases: The Spine View, Part II

Maria Pilar Aparisi Gómez
1   Department of Radiology, Auckland City Hospital, Auckland, New Zealand
2   Department of Radiology, IMSKE, Valencia, Spain
Amanda Isaac
3   School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Danoob Dalili
4   Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), Epsom, London, United Kingdom
5   Department of Diagnostic and Interventional Radiology, Epsom and St. Helier University Hospitals NHS Trust, London, United Kingdom
Anastasia Fotiadou
6   Consultant Radiologist, Royal National Orthopaedic Hospital, Stanmore, United Kingdom
Eleni P. Kariki
7   Manchester University NHS Foundation Trust, Manchester, United Kingdom
8   Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
Jan S. Kirschke
9   Interventional und Diagnostic Neuroradiology, School of Medicine, Technical University Munich, Munich, Germany
Christian R Krestan
10   Department of Radiology, Favoriten Hospital, Vienna, Austria
Carmelo Messina
11   IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
Edwin H.G. Oei
12   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
Catherine M. Phan
13   Service de Radiologie Ostéo-Articulaire, APHP, Nord-Université de Paris, Hôpital Lariboisière, Paris, France
Mahesh Prakash
14   Department of Radiodiagnosis & Imaging, PGIMER, Chandigarh, India
Nuran Sabir
15   Department of Radiology, Pamukkale University School of Medicine, Denizli, Turkey
Alberto Tagliafico
16   DISSAL, University of Genova, Genova, Italy
17   Ospedale Policlinico San Martino, Genova, Italy
Francisco Aparisi
18   Department of Radiology, Hospital Vithas Nueve de Octubre, Valencia, Spain
Thomas Baum
19   Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
Thomas M. Link
20   Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California
Giuseppe Guglielmi
21   Department of Radiology, University of Foggia, Foggia, Italy
Alberto Bazzocchi
22   Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
› Institutsangaben


Metabolic bone diseases comprise a wide spectrum. Osteoporosis, the most frequent, characteristically involves the spine, with a high impact on health care systems and on the morbidity of patients due to the occurrence of vertebral fractures (VFs).

Part II of this review completes an overview of state-of-the-art techniques on the imaging of metabolic bone diseases of the spine, focusing on specific populations and future perspectives. We address the relevance of diagnosis and current status on VF assessment and quantification. We also analyze the diagnostic techniques in the pediatric population and then review the assessment of body composition around the spine and its potential application. We conclude with a discussion of the future of osteoporosis screening, through opportunistic diagnosis and the application of artificial intelligence.


Artikel online veröffentlicht:
14. September 2022

© 2022. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

  • References

  • 1 Williams AL, Al-Busaidi A, Sparrow PJ, Adams JE, Whitehouse RW. Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur J Radiol 2009; 69 (01) 179-183
  • 2 Gossner J. Missed incidental vertebral compression fractures on computed tomography imaging: more optimism justified. World J Radiol 2010; 2 (12) 472-473
  • 3 Bauer JS, Müller D, Ambekar A. et al. Detection of osteoporotic vertebral fractures using multidetector CT. Osteoporos Int 2006; 17 (04) 608-615
  • 4 Li Y, Zhang Y, Zhang E. et al. Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. Eur Radiol 2021; 31 (12) 9612-9619
  • 5 Fink HA, Milavetz DL, Palermo L. et al; Fracture Intervention Trial Research Group. What proportion of incident radiographic vertebral deformities is clinically diagnosed and vice versa?. J Bone Miner Res 2005; 20 (07) 1216-1222
  • 6 Oei L, Rivadeneira F, Ly F. et al. Review of radiological scoring methods of osteoporotic vertebral fractures for clinical and research settings. Eur Radiol 2013; 23 (02) 476-486
  • 7 Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 1993; 8 (09) 1137-1148
  • 8 Jiang G, Eastell R, Barrington NA, Ferrar L. Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis. Osteoporos Int 2004; 15 (11) 887-896
  • 9 Lentle B, Koromani F, Brown JP. et al; Vertebral Fracture Research Groups of the CaMos, STOPP, and Rotterdam Studies. The radiology of osteoporotic vertebral fractures revisited. J Bone Miner Res 2019; 34 (03) 409-418
  • 10 Wáng YXJ, Che-Nordin N, Deng M. et al. Osteoporotic vertebral deformity with endplate/cortex fracture is associated with higher further vertebral fracture risk: the Ms. OS (Hong Kong) study results. Osteoporos Int 2019; 30 (04) 897-905
  • 11 Prior JC, Oei EHG, Brown JP, Oei L, Koromani F, Lentle BC. Where's the break? Critique of radiographic vertebral fracture diagnostic methods. Osteoporos Int 2021; 32 (12) 2391-2395
  • 12 Oei L, Koromani F, Breda SJ. et al. Osteoporotic vertebral fracture prevalence varies widely between qualitative and quantitative radiological assessment methods: the Rotterdam study. J Bone Miner Res 2018; 33 (04) 560-568
  • 13 Di Iorgi N, Maruca K, Patti G, Mora S. Update on bone density measurements and their interpretation in children and adolescents. Best Pract Res Clin Endocrinol Metab 2018; 32 (04) 477-498
  • 14 Shuhart CR, Yeap SS, Anderson PA. et al. Executive Summary of the 2019 ISCD Position Development Conference on Monitoring Treatment, DXA Cross-calibration and Least Significant Change, Spinal Cord Injury, Peri-prosthetic and Orthopedic Bone Health, Transgender Medicine, and Pediatrics. J Clin Densitom 2019; 22 (04) 453-471
  • 15 Binkovitz LA, Henwood MJ. Pediatric DXA: technique and interpretation. Pediatr Radiol 2007; 37 (01) 21-31
  • 16 Ní Bhuachalla ÉB, Daly LE, Power DG, Cushen SJ, MacEneaney P, Ryan AM. Computed tomography diagnosed cachexia and sarcopenia in 725 oncology patients: is nutritional screening capturing hidden malnutrition?. J Cachexia Sarcopenia Muscle 2018; 9 (02) 295-305
  • 17 Shen W, Punyanitya M, Wang Z. et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (1985) 2004; 97 (06) 2333-2338
  • 18 Troschel AS, Troschel FM, Best TD. et al. Computed tomography-based body composition analysis and its role in lung cancer care. J Thorac Imaging 2020; 35 (02) 91-100
  • 19 Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019; 74 (10) 1671-1678
  • 20 Löffler MT, Kallweit M, Niederreiter E. et al. Epidemiology and reporting of osteoporotic vertebral fractures in patients with long-term hospital records based on routine clinical CT imaging. Osteoporos Int 2022; 33 (03) 685-694
  • 21 Löffler MT, Sollmann N, Mei K. et al. X-ray-based quantitative osteoporosis imaging at the spine. Osteoporos Int 2020; 31 (02) 233-250
  • 22 Sekuboyina A, Bayat A, Husseini ME. et al. VerSe: A vertebrae labelling and segmentation benchmark. Med Image Anal 2021; 73: 102166 DOI: 10.1016/ . Epub 2021 Jul 22
  • 23 Löffler MT, Jacob A, Scharr A. et al. Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA. Eur Radiol 2021; 31 (08) 6069-6077
  • 24 Dagan N, Elnekave E, Barda N. et al. Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med 2020; 26 (01) 77-82
  • 25 Schwaiger BJ, Gersing AS, Baum T, Noël PB, Zimmer C, Bauer JS. Bone mineral density values derived from routine lumbar spine multidetector row CT predict osteoporotic vertebral fractures and screw loosening. AJNR Am J Neuroradiol 2014; 35 (08) 1628-1633
  • 26 Engelke K, Keaveny TM. Letter to the editor. Br J Radiol 2019; 92 (1099): 20190115
  • 27 Rühling S, Navarro F, Sekuboyina A. et al. Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements. Eur Radiol 2022; 32 (03) 1465-1474
  • 28 Roski F, Hammel J, Mei K. et al. Opportunistic osteoporosis screening: contrast-enhanced dual-layer spectral CT provides accurate measurements of vertebral bone mineral density. Eur Radiol 2021; 31 (05) 3147-3155
  • 29 Aggarwal V, Maslen C, Abel RL. et al. Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation. Ther Adv Musculoskelet Dis 2021; 13: X211024029
  • 30 Yoon AP, Lee YL, Kane RL, Kuo CF, Lin C, Chung KC. Development and validation of a deep learning model using convolutional neural networks to identify scaphoid fractures in radiographs. JAMA Netw Open 2021; 4 (05) e216096
  • 31 Kalmet PHS, Sanduleanu S, Primakov S. et al. Deep learning in fracture detection: a narrative review. Acta Orthop 2020; 91 (02) 215-220
  • 32 Dalili D, Bazzocchi A, Dalili DE, Guglielmi G, Isaac A. The role of body composition assessment in obesity and eating disorders. Eur J Radiol 2020; 131: 109227
  • 33 Chen Y, Yang T, Gao X, Xu A. Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis. Front Med 2021 August 26 (Epub ahead of print)
  • 34 Zhang B, Yu K, Ning Z. et al. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: a multicenter retrospective cohort study. Bone 2020; 140: 115561
  • 35 Cohen A, Foldes AJ, Hiller N, Simanovsky N, Szalat A. Opportunistic screening for osteoporosis and osteopenia by routine computed tomography scan: a heterogeneous, multiethnic, middle-eastern population validation study. Eur J Radiol 2021; 136: 109568
  • 36 Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network. Eur Radiol 2020; 30 (06) 3549-3557
  • 37 Adams JW, Zhang Z, Noetscher GM, Nazarian A, Makarov SN. Application of a neural network classifier to radiofrequency-based osteopenia/osteoporosis screening. IEEE J Transl Eng Health Med 2021; 9: 4900907
  • 38 Chen Z, Luo W, Zhang Q. et al. Osteoporosis diagnosis based on ultrasound radio frequency signal via multi-channel convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021: 832-835
  • 39 Rastegar S, Vaziri M, Qasempour Y. et al. Radiomics for classification of bone mineral loss: a machine learning study. Diagn Interv Imaging 2020; 101 (09) 599-610
  • 40 Roux C, Rozes A, Reizine D. et al. Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150,000 routine computed tomography scans. Rheumatology (Oxford) 2021; keab878
  • 41 Jang M, Kim M, Bae SJ, Lee SH, Koh JM, Kim N. Opportunistic osteoporosis screening using chest radiographs with deep learning: development and external validation with a cohort dataset. J Bone Miner Res 2022; 37 (02) 369-377
  • 42 Liu L, Si M, Ma H. et al. A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images. BMC Bioinformatics 2022; 23 (01) 63
  • 43 Sobecki J, Weigman B, Anderson-Carter I. et al. Opportunistic osteoporosis screening using routine computed tomography images to identify bone loss in gynecologic cancer survivors. Int J Gynecol Cancer 2022 January 31 (Epub ahead of print)
  • 44 Park SY, Ha HI, Lee SM, Lee IJ, Lim HK. Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT. PLoS One 2022; 17 (01) e0262025
  • 45 O'Gorman CA, Milne S, Lambe G, Sobota A, Beddy P, Gleeson N. Accuracy of opportunistic bone mineral density assessment on staging computed tomography for gynaecological cancers. Medicina (Kaunas) 2021; 57 (12) 1386
  • 46 Sharma GB, Robertson DD, Laney DA, Gambello MJ, Terk M. Machine learning based analytics of micro-MRI trabecular bone microarchitecture and texture in type 1 Gaucher disease. J Biomech 2016; 49 (09) 1961-1968
  • 47 Eller-Vainicher C, Zhukouskaya VV, Tolkachev YV. et al. Low bone mineral density and its predictors in type 1 diabetic patients evaluated by the classic statistics and artificial neural network analysis. Diabetes Care 2011; 34 (10) 2186-2191
  • 48 Offiah AC. Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology. Pediatr Radiol 2021 July 16 (Epub ahead of print)
  • 49 Huynh QTV, Le NQK, Huang SY. et al. Development and validation of clinical diagnostic model for girls with central precocious puberty: machine-learning approaches. PLoS One 2022; 17 (01) e0261965
  • 50 Lee H, Tajmir S, Lee J. et al. Fully automated deep learning system for bone age assessment. J Digit Imaging 2017; 30 (04) 427-441
  • 51 Tajmir SH, Lee H, Shailam R. et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol 2019; 48 (02) 275-283
  • 52 Kruse C. The new possibilities from “big data” to overlooked associations between diabetes, biochemical parameters, glucose control, and osteoporosis. Curr Osteoporos Rep 2018; 16 (03) 320-324