Skip to main content
SpringerLink
Log in

MRI-based vertebral bone quality score: relationship with age and reproducibility

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

Vertebral bone quality (VBQ) score is an opportunistic measure of bone mineral density using routine preoperative MRI in spine surgery. VBQ score positively correlates with age and is reproducible across serial scans. However, extrinsic factors, including MRI machine and protocol, affect the VBQ score and must be standardized.

Purpose

The purposes of this study were to determine whether VBQ score increased with age and whether VBQ remained consistent across serial MRI studies obtained within 3 months.

Methods

This retrospective study evaluated 136 patients, age 20–69, who received two T1-weighted lumbar MRI within 3 months of each other between January 2011 and December 2021. VBQ(L1-4) score was calculated as the quotient of L1–L4 signal intensity (SI) and L3 cerebral spinal fluid (CSF) SI. VBQ(L1) score was calculated as the quotient of L1 SI and L1 CSF SI. Regression analysis was performed to determine correlation of VBQ(L1-4) score with age. Coefficient of variation (CV) was used to determine reproducibility between VBQ(L1-4) scores from serial MRI scans.

Results

One hundred thirty-six patients (mean ± SD age 44.9 ± 12.5 years; 53.7% female) were included in this study. Extrinsic factors affecting the VBQ score included patient age, MRI relaxation time, and specific MRI machine. When controlling for MRI relaxation/echo time, the VBQ(L1-4) score was positively correlated with age and had excellent reproducibility in serial MRI with CV of 0.169. There was excellent agreement (ICC > 0.9) of VBQ scores derived from the two formulas, VBQ(L1) and VBQ(L1-4).

Conclusion

Extrinsic factors, including MRI technical factors and age, can impact the VBQ(L1-4) score and must be considered when using this tool to estimate bone mineral density (BMD). VBQ(L1-4) score was positively correlated with age. Reproducibility of the VBQ(L1-4) score across serial MRI is excellent especially when controlling for technical factors, supporting use of the VBQ score in estimating BMD. The VBQ(L1) score was a reliable alternative to the VBQ(L1-4) score.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data generated and analyzed for this study are not openly available due to the datasets containing patient identifying information. The data are available from the corresponding author on reasonable request.

References

  1. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis and T (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA J Am Med Assoc 285:785–795. https://doi.org/10.1001/jama.285.6.785

    Article  Google Scholar 

  2. Bjerke BT, Zarrabian M, Aleem IS et al (2018) Incidence of osteoporosis-related complications following posterior lumbar fusion. Glob Spine J 8:563–569. https://doi.org/10.1177/2192568217743727

    Article  Google Scholar 

  3. Russell LA (2013) Osteoporosis and orthopedic surgery: effect of bone health on total joint arthroplasty outcome. Curr Rheumatol Rep 15:371. https://doi.org/10.1007/s11926-013-0371-x

    Article  PubMed  Google Scholar 

  4. Huang C-C, Jiang C-C, Hsieh C-H et al (2016) Local bone quality affects the outcome of prosthetic total knee arthroplasty. J Orthop Res 34:240–248. https://doi.org/10.1002/jor.23003

    Article  CAS  PubMed  Google Scholar 

  5. Inoue G, Ueno M, Nakazawa T et al (2014) Teriparatide increases the insertional torque of pedicle screws during fusion surgery in patients with postmenopausal osteoporosis. J Neurosurg Spine 21:425–431. https://doi.org/10.3171/2014.5.SPINE13656

    Article  PubMed  Google Scholar 

  6. Keaveny TM, Crittenden DB, Bolognese MA et al (2017) Greater gains in spine and hip strength for romosozumab compared with teriparatide in postmenopausal women with low bone mass. J Bone Miner Res 32:1956–1962. https://doi.org/10.1002/jbmr.3176

    Article  CAS  PubMed  Google Scholar 

  7. Genant HK, Engelke K, Bolognese MA et al (2017) Effects of romosozumab compared with teriparatide on bone density and mass at the spine and hip in postmenopausal women with low bone mass. J Bone Miner Res 32:181–187. https://doi.org/10.1002/jbmr.2932

    Article  CAS  PubMed  Google Scholar 

  8. Bilezikian JP, Hattersley G, Fitzpatrick LA et al (2018) Abaloparatide-SC improves trabecular microarchitecture as assessed by trabecular bone score (TBS): a 24-week randomized clinical trial. Osteoporos Int 29:323–328. https://doi.org/10.1007/s00198-017-4304-9

    Article  CAS  PubMed  Google Scholar 

  9. Moreira CA, Fitzpatrick LA, Wang Y, Recker RR (2017) Effects of abaloparatide-SC (BA058) on bone histology and histomorphometry: the active phase 3 trial. Bone 97:314–319. https://doi.org/10.1016/j.bone.2016.11.004

    Article  CAS  PubMed  Google Scholar 

  10. Compston J (2006) Bone quality: what is it and how is it measured? Arq Bras Endocrinol Metabol 50:579–585. https://doi.org/10.1590/S0004-27302006000400003

    Article  PubMed  Google Scholar 

  11. Dipaola CP, Bible JE, Biswas D et al (2009) Survey of spine surgeons on attitudes regarding osteoporosis and osteomalacia screening and treatment for fractures, fusion surgery, and pseudoarthrosis. Spine J 9:537–544. https://doi.org/10.1016/j.spinee.2009.02.005

    Article  PubMed  Google Scholar 

  12. Jang S, Graffy PM, Ziemlewicz TJ et al (2019) Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults. Radiology 291:360–367. https://doi.org/10.1148/radiol.2019181648

    Article  PubMed  Google Scholar 

  13. Bandirali M, Di Leo G, Papini GDE et al (2015) A new diagnostic score to detect osteoporosis in patients undergoing lumbar spine MRI. Eur Radiol 25:2951–2959. https://doi.org/10.1007/s00330-015-3699-y

    Article  PubMed  Google Scholar 

  14. Saad MM, Ahmed AT, Mohamed KE, Habba MR (2019) Role of lumbar spine signal intensity measurement by MRI in the diagnosis of osteoporosis in post-menopausal women. Egypt J Radiol Nucl Med 50:35. https://doi.org/10.1186/s43055-019-0046-3

  15. Ehresman J, Pennington Z, Schilling A et al (2020) Novel MRI-based score for assessment of bone density in operative spine patients. Spine J 20:556–562. https://doi.org/10.1016/j.spinee.2019.10.018

    Article  PubMed  Google Scholar 

  16. Momeni M, Asadzadeh M, Mowla K et al (2020) Sensitivity and specificity assessment of DWI and ADC for the diagnosis of osteoporosis in postmenopausal patients. Radiol Med 125:68–74. https://doi.org/10.1007/s11547-019-01080-2

    Article  PubMed  Google Scholar 

  17. Kadri A, Binkley N, Hernando D, Anderson PA (2021) Opportunistic use of lumbar magnetic resonance imaging for osteoporosis screening. Osteoporos Int. https://doi.org/10.1007/s00198-021-06129-5

    Article  PubMed  Google Scholar 

  18. Mierke A, Ramos O, Macneille R et al (2022) Intra- and inter-observer reliability of the novel vertebral bone quality score. Eur Spine J. https://doi.org/10.1007/s00586-021-07096-5

    Article  PubMed  Google Scholar 

  19. Roch PJ, Çelik B, Jäckle K et al (2023) Combination of vertebral bone quality scores from different magnetic resonance imaging sequences improves prognostic value for the estimation of osteoporosis. Spine J 23:305–311. https://doi.org/10.1016/j.spinee.2022.10.013

    Article  PubMed  Google Scholar 

  20. Haffer H, Muellner M, Chiapparelli E et al (2022) Bone quality in patients with osteoporosis undergoing lumbar fusion surgery: analysis of the MRI-based vertebral bone quality score and the bone microstructure derived from microcomputed tomography. Spine J 22:1642–1650. https://doi.org/10.1016/j.spinee.2022.05.008

    Article  PubMed  Google Scholar 

  21. Salzmann SN, Okano I, Jones C et al (2022) Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. Spine J 22:1301–1308. https://doi.org/10.1016/j.spinee.2022.03.006

    Article  PubMed  Google Scholar 

  22. Kim AYE, Lyons K, Sarmiento M et al (2023) MRI-based score for assessment of bone mineral density in operative spine patients. Spine (Phila Pa 1976) 48:107–112. https://doi.org/10.1097/BRS.0000000000004509

  23. Ehresman J, Ahmed AK, Lubelski D et al (2020) Vertebral bone quality score and postoperative lumbar lordosis associated with need for reoperation after lumbar fusion. World Neurosurg 140:e247–e252. https://doi.org/10.1016/j.wneu.2020.05.020

    Article  PubMed  Google Scholar 

  24. Chen Z, Lei F, Ye F et al (2023) Prediction of pedicle screw loosening using an MRI-based vertebral bone quality score in patients with lumbar degenerative disease. World Neurosurg 171:e760–e767. https://doi.org/10.1016/j.wneu.2022.12.098

    Article  PubMed  Google Scholar 

  25. Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6:284–290. https://doi.org/10.1037/1040-3590.6.4.284

    Article  Google Scholar 

  26. Aynaszyan S, Devia LG, Udoeyo IF et al (2022) Patient physiology influences the MRI-based vertebral bone quality score. Spine J 22:1866–1874. https://doi.org/10.1016/j.spinee.2022.06.003

    Article  PubMed  Google Scholar 

  27. Bredella MA, Torriani M, Ghomi RH et al (2011) Vertebral bone marrow fat is positively associated with visceral fat and inversely associated with IGF-1 in obese women. Obesity 19:49–53. https://doi.org/10.1038/oby.2010.106

    Article  CAS  PubMed  Google Scholar 

  28. Warming L, Hassager C, Christiansen C (2002) Changes in bone mineral density with age in men and women: a longitudinal study. Osteoporos Int 13:105–112. https://doi.org/10.1007/s001980200001

    Article  CAS  PubMed  Google Scholar 

  29. Aparisi Gómez MP, AyusoBenavent C, Simoni P et al (2020) Fat and bone: the multiperspective analysis of a close relationship. Quant Imaging Med Surg 10:1614–1635. https://doi.org/10.21037/qims.2020.01.11

    Article  PubMed  PubMed Central  Google Scholar 

  30. Baum T, Rohrmeier A, Syväri J et al (2018) Anatomical variation of age-related changes in vertebral bone marrow composition using chemical shift encoding-based water–fat magnetic resonance imaging. Front Endocrinol (Lausanne) 9:141. https://doi.org/10.3389/fendo.2018.00141

  31. Sieron D, Drakopoulos D, Loebelenz LI et al (2020) Correlation between fat signal ratio on T1-weighted MRI in the lower vertebral bodies and age, comparing 1.5-T and 3-T scanners. Acta Radiol Open 9:205846012090151. https://doi.org/10.1177/2058460120901517

    Article  Google Scholar 

  32. Chang R, Ma X, Jiang Y et al (2020) Percentage fat fraction in magnetic resonance imaging: upgrading the osteoporosis-detecting parameter. BMC Med Imaging 20:30. https://doi.org/10.1186/s12880-020-00423-0

    Article  PubMed  PubMed Central  Google Scholar 

  33. Garner HW, Paturzo MM, Gaudier G et al (2017) Variation in attenuation in L1 trabecular bone at different tube voltages: caution is warranted when screening for osteoporosis with the use of opportunistic CT. AJR Am J Roentgenol 208:165–170. https://doi.org/10.2214/AJR.16.16744

    Article  PubMed  Google Scholar 

  34. Ehresman J, Schilling A, Pennington Z et al (2020) A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. J Neurosurg Spine 32:499–506. https://doi.org/10.3171/2019.9.SPINE19954

    Article  Google Scholar 

  35. Ehresman J, Schilling A, Yang X et al (2021) Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J 21:20–27. https://doi.org/10.1016/j.spinee.2020.05.540

    Article  PubMed  Google Scholar 

  36. Pickhardt PJ, Nguyen T, Perez AA et al (2022) Improved CT-based osteoporosis assessment with a fully automated deep learning tool. Radiol Artif Intell 4:5. https://doi.org/10.1148/ryai.220042

Download references

Acknowledgements

We would like to thank Sam Mosiman and Diane Krueger for their consultation on statistical methods.

Author information

Authors and Affiliations

  1. Department of Orthopedics and Rehabilitation, School of Medicine and Public Health, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792-3252, USA

    Daniel Liu & Paul A. Anderson

  2. Department of Orthopaedic Surgery and Sports Medicine, University of Kentucky College of Medicine, Lexington, KY, USA

    Aamir Kadri

  3. Department of Radiology and Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA

    Diego Hernando

  4. Osteoporosis Clinical Research Program, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA

    Neil Binkley

Authors
  1. Daniel Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aamir Kadri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego Hernando
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neil Binkley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paul A. Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Liu.

Ethics declarations

Conflict of interest

Daniel Liu, Aamir Kadri, and Dr. Binkley declare that they have no conflict of interest. Dr. Hernando is a co-founder of Calimetrix, LLC. Dr. Anderson reports personal fees from Radius Medical, Amgen, and Medtronic and stock interest in Titan Spine, outside of this submitted work.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 20 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, D., Kadri, A., Hernando, D. et al. MRI-based vertebral bone quality score: relationship with age and reproducibility. Osteoporos Int 34, 2077–2086 (2023). https://doi.org/10.1007/s00198-023-06893-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-023-06893-6

Keywords

Search

Navigation