Radiofrequency echographic multi-spectrometry for the in-vivo assessment of bone strength: state of the art—outcomes of an expert consensus meeting organized by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO)

Aging Clinical and Experimental Research - Tập 31 Số 10 - Trang 1375-1389 - 2019
Adolfo Díez-Pérez1, Maria Luisa Brandi2, Nasser M. Al-Daghri3, Jaime Branco4, Olivier Bruyère5, Loredana Cavalli6, Cyrus Cooper7, Bernard Cortet8, Bess Dawson‐Hughes9, Hans Peter Dimai10, Stefano Gonnelli11, Peyman Hadji12, Philippe Halbout13, Jean Kaufman14, Andreas Kurth15, Médéa Locquet16, Stefania Maggi17, Radmila Matijević18, Jean Yves Reginster5, René Rizzoli19, Thierry Thomas20
1Department of Internal Medicine, Hospital del Mar/IMIM and CIBERFES, Autonomous University of Barcelona, Passeig Maritim 25-29, 08003, Barcelona, Spain
2FirmoLab Fondazione F.I.R.M.O., Florence, Italy
3Chair for Biomarkers of Chronic Diseases, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
4NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
5WHO Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, University of Liège, Liège, Belgium
6Department of Biological, Experimental and Clinical Science, University of Florence, Florence, Italy
7MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Southampton, UK
8Department of Rheumatology and EA 4490, University-Hospital of Lille, Lille, France
9Bone Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
10Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
11Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
12Frankfurter Hormon und Osteoporose Zentrum, Frankfurt, Germany
13International Osteoporosis Foundation, Nyon, Switzerland
14Department of Endocrinology, Ghent University Hospital, Ghent, Belgium
15Department of Orthopaedic Surgery and Osteology, Klinikum Frankfurt, Frankfurt, Germany.
16Department of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium
17National Research Council, Aging Program, Institute of Neuroscience, Padua, Italy
18Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia
19Service of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
20Department of Rheumatology, Hospital Nord, CHU St Etienne, St Etienne, France

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Anonymous (1993) Consensus development conference: diagnosis, prophylaxis and treatment of osteoporosis. Am J Med 94:646–650

Armas LAG, Lappe JM, Heaney RP (2010) Calcium, bone strength and fractures. In: Orwoll ES, Bilezikian JP, Vanderschueren D (eds) Osteoporosis in men, 2nd edn. Academic Press, London, pp 235–241

Choksi P, Jepsen KJ, Clines GA (2018) The challenges of diagnosing osteoporosis and the limitations of currently available tools. Clin Diabetes Endocrinol 4:12. https://doi.org/10.1186/s40842-018-0062-7

Fonseca H, Moreira-Gonçalves D, Coriolano HJ et al (2014) Bone quality: the determinants of bone strength and fragility. Sports Med 44:37–53. https://doi.org/10.1007/s40279-013-0100-7

Amman P, Rizzoli R (2003) Bone strength and its determinants. Osteoporos Int 14:13. https://doi.org/10.1007/s00198-002-1345-4

Hart NH, Nimphius S, Rantalainen T et al (2017) Mechanical basis of bone strength: influence of bone material, bone structure and muscle action. J Musculoskelet Neuronal Interact 17:114–139

Fogelman I, Blake GM (2000) Different approaches to bone densitometry. J Nucl Med 41:2015–2025

Damilakis J, Adams JE, Guglielmi G et al (2010) Radiation exposure in X-ray-based imaging techniques used in osteoporosis. Eur Radiol 20:2707–2714. https://doi.org/10.1007/s00330-010-1845-0

[No authors listed] (2003) Prevention and management of osteoporosis. World Health Organ Tech Rep Ser 921:1–164

Bousson V, Le Bras A, Roqueplan F et al (2006) Volumetric quantitative computed tomography of the proximal femur: relationships linking geometric and densitometric variables to bone strength. Role for compact bone. Osteoporos Int 17:855–864

World Health Organization (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group [meeting held in Rome from 22 to 25 June 1992]

Kanis JA, Melton LJ, Christiansen C et al (1994) The diagnosis of osteoporosis. J Bone Miner Res 9:1137–1141

Blake G, Adams JE, Bishop N (2013) DXA in adults and children. In: Rosen CJ, Delmas P (eds) Primer on the metabolic bone diseases and disorders of mineral metabolism, 8th edn. American Society of Bone and Mineral Research (ASBMR), Washington, pp 152–158

Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312:1254–1259

Leslie WD, Majumdar SR, Morin SN et al (2015) Why does rate of bone density loss not predict fracture risk? J Clin Endocrinol Metab 100:679–683. https://doi.org/10.1210/jc.2014-3777

Siris ES, Chen YT, Abbott TA et al (2004) Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med 164:1108–1112

Unnanuntana A, Gladnick BP, Donnelly E et al (2010) The assessment of fracture risk. J Bone Joint Surg Am 92:743–753

Wainwright SA, Marshall LM, Ensrud KE et al (2005) Hip fracture in women without osteoporosis. J Clin Endocrinol Metab 90:2787–2793

Schacter GI, Leslie WD (2017) DXA-based measurements in diabetes: can they predict fracture risk? Calcif Tissue Int 100:150–164. https://doi.org/10.1007/s00223-016-0191-x

Kanis JA, Borgstrom F, De Laet C et al (2005) Assessment of fracture risk. Osteoporos Int 16:581–589

Müller D, Pulm J, Gandjour A (2012) Cost-effectiveness of different strategies for selecting and treating individuals at increased risk of osteoporosis or osteopenia: a systematic review. Value Health 15:284–298. https://doi.org/10.1016/j.jval.2011.11.030

Nayak S, Roberts MS, Greenspan SL (2011) Cost-effectiveness of different screening strategies for osteoporosis in postmenopausal women. Ann Intern Med 155:751–761

Pothuaud L, Carceller P, Hans D (2008) Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone 42:775–787. https://doi.org/10.1016/j.bone.2007.11.018

Silva BC, Leslie WD, Resch H et al (2014) Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res 29:518–530. https://doi.org/10.1002/jbmr.2176

Hans D, Goertzen AL, Krieg MA et al (2011) Bone microarchitecture assessed by TBS predicts osteoporotic fractures independent of bone density: the Manitoba study. J Bone Miner Res 26:2762–2769. https://doi.org/10.1002/jbmr.499

Leslie WD, Majumdar SR, Morin SN et al (2017) Change in trabecular bone score (TBS) with antiresorptive therapy does not predict fracture in women: the Manitoba BMD cohort. J Bone Miner Res 32:618–623. https://doi.org/10.1002/jbmr.3054

Leslie WD, Aubry-Rozier B, Lamy O et al (2013) TBS (trabecular bone score) and diabetes-related fracture risk. J Clin Endocrinol Metab 98:602–609. https://doi.org/10.1210/jc.2012-3118

Martineau P, Silva BC, Leslie WD (2017) Utility of trabecular bone score in the evaluation of osteoporosis. Curr Opin Endocrinol Diabetes Obes 24:402–410. https://doi.org/10.1097/MED.0000000000000365

Kanis JA, Johnell O, Oden A et al (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19:385–397. https://doi.org/10.1007/s00198-007-0543-5

McCloskey EV, Odén A, Harvey NC et al (2016) A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX. J Bone Miner Res 31:940–948

Winzenrieth R, Michelet F, Hans D (2013) Three-dimensional (3D) microarchitecture correlations with 2D projection image gray-level variations assessed by trabecular bone score using high-resolution computed tomographic acquisitions: effects of resolution and noise. J Clin Densitom 16:287–296

Chen W, Slattery A, Center J et al (2016) The effect of changing scan mode on trabecular bone score using lunar prodigy. J Clin Densitom 19:502–506

Amnuaywattakorn S, Sritara C, Utamakul C et al (2016) Simulated increased soft tissue thickness artefactually decreases trabecular bone score: a phantom study. BMC Musculoskelet Disord 17:17. https://doi.org/10.1186/s12891-016-0886-1

Schousboe JT, Vo TN, Langsetmo L et al (2017) Association of trabecular bone score (TBS) with incident clinical and radiographic vertebral fractures adjusted for lumbar spine BMD in older men: a prospective cohort study. J Bone Miner Res 32:1554–1558. https://doi.org/10.1002/jbmr.3130

Langsetmo L, Vo TN, Ensrud KE et al (2016) The association between trabecular bone score and lumbar spine volumetric BMD is attenuated among older men with high body mass index. J Bone Miner Res 31:1820–1826. https://doi.org/10.1002/jbmr.2867

Roux JP, Wegrzyn J, Boutroy S, Bouxsein ML et al (2013) The predictive value of trabecular bone score (TBS) on whole lumbar vertebrae mechanics: an ex vivo study. Osteoporos Int 24:2455–2460. https://doi.org/10.1007/s00198-013-2316-7

Maquer G, Lu Y, Dall’Ara E et al (2016) The initial slope of the variogram, foundation of the trabecular bone score, is not or is poorly associated with vertebral strength. J Bone Miner Res 31:341–346. https://doi.org/10.1002/jbmr.2610

Harvey NC, Glüer CC, Binkley N et al (2015) Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice. Bone 78:216–224. https://doi.org/10.1016/j.bone.2015.05.016

Edmondson CP, Schwartz EN (2017) Non-BMD DXA measurements of the hip. Bone 104:73–83. https://doi.org/10.1016/j.bone.2017.03.050

Leslie WD, Lix LM, Morin SN et al (2016) Adjusting hip fracture probability in men and women using hip axis length: the Manitoba bone density database. J Clin Densitom 19:326–331. https://doi.org/10.1016/j.jocd.2015.07.004

Broy SB, Cauley JA, Lewiecki ME et al (2015) Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD Official Positions Part 1: hip geometry. J Clin Densitom 18:287–308. https://doi.org/10.1016/j.jocd.2015.06.005

Beck TJ, Broy SB (2015) Measurement of hip geometry—technical background. J Clin Densitom 18:331–337. https://doi.org/10.1016/j.jocd.2015.06.006

Zhuang H, Li Y, Lin J et al (2017) Cortical thickness in the intertrochanteric region may be relevant to hip fracture type. BMC Musculoskelet Disord 18:305. https://doi.org/10.1186/s12891-017-1669-z

Lee DH, Jung KY, Hong AR et al (2016) Femoral geometry, bone mineral density, and the risk of hip fracture in premenopausal women: a case control study. BMC Musculoskelet Disord 17:42. https://doi.org/10.1186/s12891-016-0893-2

Adams JE (2009) Quantitative computed tomography. Eur J Radiol 71:415–424

Link TM, Lang TF (2014) Axial QCT: clinical applications and new developments. J Clin Densitom 17:438–448. https://doi.org/10.1016/j.jocd.2014.04.119

Manhard MK, Nyman JS, Does MD (2017) Advances in imaging approaches to fracture risk evaluation. Transl Res 181:1–14. https://doi.org/10.1016/j.trsl.2016.09.006

Kopperdahl DL, Aspelund T, Hoffmann PF et al (2014) Assessment of incident spine and hip fractures in women and men using finite element analysis of CT scans. J Bone Miner Res 29:570–580. https://doi.org/10.1002/jbmr.2069

Keaveny TM, Hoffmann PF, Singh M et al (2008) Femoral bone strength and its relation to cortical and trabecular changes after treatment with PTH, alendronate, and their combination as assessed by finite element analysis of quantitative CT scans. J Bone Miner Res 23:1974–1982. https://doi.org/10.1359/jbmr.080805

Areeckal AS, Kocher M, Sumam David S (2018) Current and emerging diagnostic imaging-based techniques for assessment of osteoporosis and fracture risk. IEEE Rev Biomed Eng 12:254–268. https://doi.org/10.1109/RBME.2018.2852620

Ruiz Wills C, Olivares AL, Tassani S et al (2019) 3D patient-specific finite element models of the proximal femur based on DXA towards the classification of fracture and non-fracture cases. Bone 121:89–99. https://doi.org/10.1016/j.bone.2019.01.001

Yang S, Leslie WD, Luo Y et al (2018) Automated DXA-based finite element analysis for hip fracture risk stratification: a cross-sectionalstudy. Osteoporos Int 29:191–200. https://doi.org/10.1007/s00198-017-4232-8

Leslie WD, Luo Y, Yang S et al (2019) Fracture risk indices from DXA-based finite element analysis predict incident fractures independently from FRAX: The Manitoba BMD Registry. J Clin Densitom. https://doi.org/10.1016/j.jocd.2019.02.001

Stagi S, Cavalli L, Cavalli T et al (2016) Peripheral quantitative computed tomography (pQCT) for the assessment of bone strength in most of bone affecting conditions in developmental age: a review. Ital J Pediatr 42:88

MacNeil JA, Boyd SK (2008) Improved reproducibility of high-resolution peripheral quantitative computed tomography for measurement of bone quality. Med Eng Phys 30:792–799. https://doi.org/10.1016/j.medengphy.2007.11.003

Burghardt AJ, Link TM, Majumdar S (2011) High-resolution computed tomography for clinical imaging of bone microarchitecture. Clin Orthop Relat Res 469:2179–2193. https://doi.org/10.1007/s11999-010-1766-x

van Rietbergen B, Ito K (2015) A survey of micro-finite element analysis for clinical assessment of bone strength: the first decade. J Biomech 48:832–841. https://doi.org/10.1016/j.jbiomech.2014.12.024

Digby MG, Bishop NJ, Paggiosi MA et al (2016) HR-pQCT: a non-invasive ‘biopsy’ to assess bone structure and strength. Arch Dis Child Educ Pract Ed 101:268–270. https://doi.org/10.1136/archdischild-2015-309455

Lespessailles E, Ibrahim-Nasser N, Toumi H et al (2018) Contribution of high resolution peripheral quantitative CT to the management of bone and joint diseases. Joint Bone Spine 85:301–306. https://doi.org/10.1016/j.jbspin.2017.04.012

Biver E, Durosier-Izart C, Chevalley T et al (2018) Evaluation of radius microstructure and areal bone mineral density improves fracture prediction in postmenopausal women. J Bone Miner Res 33:328–337. https://doi.org/10.1002/jbmr.3299

Cheung AM, Adachi JD, Hanley DA et al (2013) High-resolution peripheral quantitative computed tomography for the assessment of bone strength and structure: a review by the Canadian Bone Strength Working Group. Curr Osteoporos Rep 11:136–146. https://doi.org/10.1007/s11914-013-0140-9

Chang G, Honig S, Liu Y et al (2015) 7 Tesla MRI of bone microarchitecture discriminates between women without and with fragility fractures who do not differ by bone mineral density. J Bone Miner Metab 33:285–293. https://doi.org/10.1007/s00774-014-0588-4

Griffith JF, Genant HK (2012) New advances in imaging osteoporosis and its complications. Endocrine 42:39–51. https://doi.org/10.1007/s12020-012-9691-2

Hans D, Baim S (2017) Quantitative ultrasound (QUS) in the management of osteoporosis and assessment of fracture risk. J Clin Densitom 20:322–333. https://doi.org/10.1016/j.jocd.2017.06.018

Shepherd JA, Schousboe JT, Broy SB et al (2015) Executive summary of the 2015 ISCD position development conference on advanced measures from DXA and QCT: fracture prediction beyond BMD. J Clin Densitom 18(3):274–286. https://doi.org/10.1016/j.jocd.2015.06.013

Langton CM, Ali AV, Riggs CM et al (1990) A contact method for the assessment of ultrasonic velocity and broadband attenuation in cortical and cancellous bone. Clin Phys Physiol Meas 11:243–249

Prins SH, Jørgensen HL, Jørgensen LV et al (1998) The role of quantitative ultrasound in the assessment of bone: a review. Clin Physiol 18:3–17

Damilakis J, Maris TG, Karantanas AH (2007) An update on the assessment of osteoporosis using radiologic techniques. Eur Radiol 17:1591–1602

Chan MY, Nguyen ND, Center JR et al (2013) Quantitative ultrasound and fracture risk prediction in non-osteoporotic men and women as defined by WHO criteria. Osteoporos Int 24:1015–1022. https://doi.org/10.1007/s00198-012-2001-2

McLeod KM, Johnson S, Rasali D et al (2015) Discriminatory performance of the calcaneal quantitative ultrasound and osteoporosis self-assessment tool to select older women for dual-energy X-ray absorptiometry. J Clin Densitom 18:157–164. https://doi.org/10.1016/j.jocd.2015.02.006

Zhang L, Lv H, Zheng H et al (2015) Correlation between parameters of calcaneal quantitative ultrasound and hip structural analysis in osteoporotic fracture patients. PLoS ONE 10:e0145879. https://doi.org/10.1371/journal.pone.0145879

Marín F, González-Macías J, Díez-Pérez A et al (2006) Relationship between bone quantitative ultrasound and fractures: a meta-analysis. J Bone Miner Res 21:1126–1135

McCloskey EV, Johansson H, Kanis JA et al (2015) Predictive ability of heel quantitative ultrasound for incident fractures: an individual-level meta-analysis. Osteoporos Int 26:1979–1987. https://doi.org/10.1007/s00198-015-3072-7

Black DM, Cauley JA, Wagman R et al (2018) The ability of a single BMD and fracture history assessment to predict fracture over 25 years in postmenopausal women: the study of osteoporotic fractures. J Bone Miner Res 33:389–395. https://doi.org/10.1002/jbmr.3194

Austin M, Yang YC, Vittinghoff E et al (2012) Relationship between bone mineral density changes with denosumab treatment and risk reduction for vertebral and nonvertebral fractures. J Bone Miner Res 27:687–693. https://doi.org/10.1002/jbmr.1472

Jacques RM, Boonen S, Cosman F et al (2012) Relationship of changes in total hip bone mineral density to vertebral and nonvertebral fracture risk in women with postmenopausal osteoporosis treated with once-yearly zoledronic acid 5 mg: the HORIZON-Pivotal Fracture Trial (PFT). J Bone Miner Res 27:1627–1634. https://doi.org/10.1002/jbmr.1644

Bouxsein ML, Eastell R, Lui LY et al (2019) Change in bone density and reduction in fracture risk: a meta-regression of published trials. J Bone Miner Res 1:1. https://doi.org/10.1002/jbmr.3641

Kanis JA, Cooper C, Rizzoli R et al (2019) European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 30:3–44. https://doi.org/10.1007/s00198-018-4704-5

Osterhoff G, Morgan EF, Shefelbine SJ et al (2016) Bone mechanical properties and changes with osteoporosis. Injury 47:S11–S20. https://doi.org/10.1016/S0020-1383(16)47003-8

McCreadie BR, Goldstein SA (2000) Biomechanics of fracture: is bone mineral density sufficient to assess risk? J Bone Miner Res 15:2305–2308

Keaveny TM, Bouxsein ML (2008) Theoretical implications of the biomechanical fracture threshold. J Bone Miner Res 23:1541–1547. https://doi.org/10.1359/jbmr.080406

Boutroy S, Bouxsein ML, Munoz F et al (2005) In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. J Clin Endocrinol Metab 90:6508–6515

Samelson EJ, Broe KE, Xu H et al (2019) Cortical and trabecular bone microarchitecture as an independent predictor of incident fracture risk in older women and men in the Bone Microarchitecture International Consortium (BoMIC): a prospective study. Lancet Diabetes Endocrinol 7:34–43. https://doi.org/10.1016/s2213-8587(18)30308-5 . Erratum in: Lancet Diabetes Endocrinol 7:e1

Eastell R, Wahner HW, O’Fallon WM et al (1989) Unequal decrease in bone density of lumbar spine and ultradistal radius in Colles’ and vertebral fracture syndromes. J Clin Invest 83:168–174

Shen J, Griffith JF, Zhu TY et al (2018) Bone mass, microstructure, and strength can discriminate vertebral fracture in patients on long-term steroid treatment. J Clin Endocrinol Metab 103:3340–3349. https://doi.org/10.1210/jc.2018-00490

Johannesdottir F, Allaire B, Bouxsein ML (2018) Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep 16:411. https://doi.org/10.1007/s11914-018-0450-z

Panyasantisuk J, Dall’Ara E, Pretterklieber M et al (2018) Mapping anisotropy improves QCT-based finite element estimation of hip strength in pooled stance and side-fall load configurations. Med Eng Phys 59:36–42. https://doi.org/10.1016/j.medengphy.2018.06.004

Szulc P, Boutroy S, Chapurlat R (2018) Prediction of fractures in men using bone microarchitectural parameters assessed by high-resolution peripheral quantitative computed tomography—the prospective STRAMBO study. J Bone Miner Res 33:1470–1479. https://doi.org/10.1002/jbmr.3451

Unal M, Creecy A, Nyman JS (2018) The role of matrix composition in the mechanical behaviour of bone. Curr Osteoporos Rep 16:205–215. https://doi.org/10.1007/s11914-018-0433-0

Herrera S, Diez-Perez A (2017) Clinical experience with microindentation in vivo in humans. Bone 95:175–182. https://doi.org/10.1016/j.bone.2016.11.003

Rozental TD, Walley KC, Demissie S et al (2018) Bone material strength index as measured by impact microindentation in postmenopausal women with distal radius and hip fractures. J Bone Miner Res 33:621–626. https://doi.org/10.1002/jbmr.3338

Winzenrieth R, Humbert L, Di Gregorio S et al (2018) Effects of osteoporosis drug treatments on cortical and trabecular bone in the femur using DXA-based 3D modeling. Osteoporos Int 29:2323–2333. https://doi.org/10.1007/s00198-018-4624-4

Bliuc D, Alarkawi D, Nguyen TV et al (2015) Risk of subsequent fractures and mortality in elderly women and men with fragility fractures with and without osteoporotic bone density: the Dubbo Osteoporosis Epidemiology Study. J Bone Miner Res 30:637–646. https://doi.org/10.1002/jbmr.2393

Viceconti M, Qasim M, Bhattacharya P et al. (2018) Are CT-based finite element model predictions of femoral bone strengthening clinically useful? Curr Osteoporos Rep 16:216–223. https://doi.org/10.1007/s11914-018-0438-8 . Review. Erratum in: Curr Osteoporos Rep. 2018 Jun 22

Kanis JA, Hiligsmann M (2014) The application of health technology assessment in osteoporosis. Best Pract Res Clin Endocrinol Metab 28:895–910. https://doi.org/10.1016/j.beem.2014.04.001

Minniti D, Davini O, Gualano MR et al (2014) Techniques for diagnosing osteoporosis: a systematic review of cost-effectiveness studies. Int J Technol Assess Health Care 30:273–281. https://doi.org/10.1017/S0266462314000257

Conversano F, Franchini R, Greco A et al (2015) A novel ultrasound methodology for estimating spine mineral density. Ultrasound Med Biol 41:281–300. https://doi.org/10.1016/j.ultrasmedbio.2014.08.017

Casciaro S, Peccarisi M, Pisani P et al (2016) An advanced quantitative echosound methodology for femoral neck densitometry. Ultrasound Med Biol 42:1337–1356. https://doi.org/10.1016/j.ultrasmedbio.2016.01.024

Caffarelli C, Tomai Pitinica MD, Francolini V et al (2018) REMS technique: future perspectives in an Academic Hospital. Clin Cases Miner Bone Metab 15:163–165

Greco A, Pisani P, Conversano F et al (2017) Ultrasound fragility score: an innovative approach for the assessment of bone fragility. Measurement 101:236–242

Pisani P, Greco A, Conversano F et al (2017) A quantitative ultrasound approach to estimate bone fragility: a first comparison with dual X-ray absorptiometry. Measurement 101:243–249

Di Paola M, Gatti D, Viapiana O et al (2018) Radiofrequency echographic multispectrometry compared with dual X-ray absorptiometry for osteoporosis diagnosis on lumbar spine and femoral neck. Osteoporos Int 30:391–402. https://doi.org/10.1007/s00198-018-4686-3

Messina C, Bandirali M, Sconfienza LM et al (2015) Prevalence and type of errors in dual-energy X-ray absorptiometry. Eur Radiol 25:1504–1511. https://doi.org/10.1007/s00330-014-3509-y

Altman DG, Bland JM (1983) Measurements in medicine: the analysis of method comparison studies. Statistician 32:307–317

Hopkins SJ, Welsman JR, Knapp KM (2014) Short-term precision error in dual energy X-ray absorptiometry, bone mineral density and trabecular bone score measurements; and effects of obesity on precision error. J Biomed Gr Comput 4:8–14

Ravaud P, Reny JL, Giraudeau B et al (1999) Individual smallest detectable difference in bone mineral density measurements. J Bone Miner Res 14:1449–1456

Ovejero Crespo D, Nogues X, Diez-Perez A (2019) The non-ionizing radiofrequency echographic multi spectrometry (REMS) applied on a Spanish cohort for the osteoporosis diagnosis on lumbar spine and femoral neck. WCO-IOF-ESCEO Abstract P795

Adami G, Arioli G, Bianchi G et al (2019) Prediction of incident fragility fractures through radiofrequency echographic multi spectrometry (REMS). Ann Rheum Dis 78(Suppl 2):933. https://doi.org/10.1136/annrheumdis-2019-eular.6256