Skip to main content
SpringerLink
Log in

Measurement of Hounsfield units on proximal femur computed tomography for predicting regional osteoporosis

Messung der Hounsfield-Einheiten in der Computertomographie am proximalen Femur zur Vorhersage regionaler Osteoporose

  • Original articles
  • Published:
Die Radiologie Aims and scope Submit manuscript

Abstract

Objective

This study was designed to investigate the use of proximal femoral Hounsfield units (HU) in conventional abdominal and pelvic computed tomography (CT) to predict hip osteoporosis by coupling with data from quantitative CT (QCT).

Methods

In this study, 315 patients who underwent routine abdominal and pelvic CT with the proximal femur included in the scanning range were also subjected to QCT of the proximal femur. Pearson correlation test was performed to analyze the correlations of the femoral head, femoral neck, proximal femur, and femoral trochanter CT HU with the femoral neck, femoral trochanter, and intertrochanteric femur bone mineral density (BMD) values from QCT. The diagnostic performance of CT HU measurement of the proximal femur for osteoporosis was analyzed using receiver operating characteristic (ROC) curves.

Results

The CT HU of the proximal femur showed the highest correlation with the BMD value of the hip (r = 0.826; p < 0.01). The mean CT HU of the proximal femur differed significantly (all p < 0.01) for the three QCT-defined BMD categories of osteoporosis (192.23 HU vs. 188.71), of osteopenia (247.86 HU vs. 248.36 HU), and of normal individuals (308.13 HU vs. 310.41 HU) in left and right sides, respectively. In the ROC curve analysis, the area under the ROC curve values to predict osteoporosis in the left and right proximal femurs were 0.942 and 0.941, respectively.

Conclusion

The CT HU of the proximal femur was significantly associated with the BMD value of the hip measured by QCT. The CT HU of the proximal femur is highly effective in diagnosing osteoporosis and could be used for hip osteoporosis screening.

Zusammenfassung

Ziel

Ziel der vorliegenden Studie war es, die Verwendung der Werte für die Hounsfield-Einheiten (HU) am proximalen Femur in der konventionellen Abdomen- und Beckencomputertomographie (CT) zur Vorhersage der Osteoporose an der Hüfte durch Kopplung mit Daten einer quantitativen CT (QCT) zu untersuchen.

Methoden

In der vorliegenden Studie wurde bei 315 Patienten, bei denen eine Routine-CT von Abdomen und Becken unter Einbezug des proximalen Femurs in den Bereich der Aufnahme erfolgte, auch eine QCT des proximalen Femurs durchgeführt. Eine Korrelationsanalyse nach Pearson wurde eingesetzt, um die Korrelationen der CT-HU von Femurkopf, Oberschenkelhals, proximalem Femur und Trochanter femoris mit den Werten der Knochendichte (BMD) für den Oberschenkelhals, Trochanter femoris und intertrochantären Femurbereich aus der QCT zu ermitteln. Die diagnostische Leistungsfähigkeit der CT-HU-Messung des proximalen Femurs in Bezug auf Osteoporose wurde unter Verwendung der Receiver-Operating-Characteristic(ROC)-Kurvenanalyse ausgewertet.

Ergebnisse

Die CT-HU-Werte des proximalen Femurs wiesen die höchste Korrelation mit den BMD-Werten der Hüfte auf (r = 0,826; p < 0,01). Die mittleren CT-HU-Werte des proximalen Femurs unterschieden sich signifikant (alle p < 0,01) bei den 3 QCT-definierten BMD-Kategorien der Osteoporose (192,23 HU vs. 188,71), der Osteopenie (247,86 HU vs. 248,36 HU) und von normalen Individuen (308,13 HU vs. 310,41 HU) auf der linken bzw. rechten Seite. In der ROC-Kurvenanalyse betrugen die Werte für die Fläche unter der ROC-Kurve zur Vorhersage von Osteoporose im linken bzw. rechten proximalen Femur 0,942 bzw. 0,941.

Schlussfolgerung

Die CT-HU-Werte des proximalen Femurs waren in signifikanter Weise mit den BMD-Werten der Hüfte aus der Messung mittels QCT assoziiert. Für die Diagnose der Osteoporose sind die CT-HU-Werte des proximalen Femurs sehr geeignet und könnten zum Screening auf Osteoporose der Hüfte eingesetzt werden.

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

Similar content being viewed by others

References

  1. (1993) Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Conference report. Am J Med 94(6):646–650

  2. Johnell O, Kanis JA (2006) An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int 17:1726–1733

    Article  CAS  PubMed  Google Scholar 

  3. Gullberg B, Johnell O, Kanis JA (1997) World-wide projections for hip fracture. Osteoporos Int 7(5):407–413. https://doi.org/10.1007/pl00004148 (PMID: 9425497)

    Article  CAS  PubMed  Google Scholar 

  4. Yusuf AA, Cummings SR, Watts NB, Feudjo MT et al (2018) Real-world effectiveness of osteoporosis therapies for fracture reduction in post-menopausal women. Arch Osteoporos 13(1):33. https://doi.org/10.1007/s11657-018-0439-3

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lim HK, Ha HI, Park SY et al (2019) Comparison of the diagnostic performance of CT Hounsfield unit histogram analysis and dual-energy X‑ray absorptiometry in predicting osteoporosis of the femur. Eur Radiol 29(4):1831–1840. https://doi.org/10.1007/s00330-018-5728-0

    Article  PubMed  Google Scholar 

  6. Wang P, She W, Mao Z et al (2021) Use of routine computed tomography scans for detecting osteoporosis in thoracolumbar vertebral bodies. Skelet Radiol 50(2):371–379. https://doi.org/10.1007/s00256-020-03573-y

    Article  Google Scholar 

  7. Wagner SC, Formby PM, Helgeson MD et al (2016) Diagnosing the undiagnosed: osteoporosis in patients undergoing lumbar fusion. Spine (phila Pa 1976) 41(21):E1279–E1283. https://doi.org/10.1097/BRS.0000000000001612

    Article  PubMed  Google Scholar 

  8. Formby PM, Kang DG, Helgeson MD et al (2016) Clinical and radiographic outcomes of transforaminal lumbar interbody fusion in patients with osteoporosis. Global Spine J 6(7):660–664. https://doi.org/10.1055/s-0036-1578804

    Article  PubMed  PubMed Central  Google Scholar 

  9. Li YL, Wong KH, Law MW et al (2018) Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population. Arch Osteoporos 13(1):76. https://doi.org/10.1007/s11657-018-0492-y

    Article  PubMed  Google Scholar 

  10. Kılınc RM, Açan AE, Türk G et al (2022) Evaluation of femoral head bone quality by Hounsfield units: a comparison with dual-energy X‑ray absorptiometry. Acta Radiol 63(7):933–941. https://doi.org/10.1177/02841851211021035

    Article  PubMed  Google Scholar 

  11. Lim HK, Ha HI, Park SY et al (2021) Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study. Plos One 16(3):e247330. https://doi.org/10.1371/journal.pone.0247330

  12. American College of Radiology (2018) ACR-SPR-SSR practice parameter for the performance of musculoskeletal quantitative computed tomography (QCT). American College of Radiology, Reston. https://www.acr.org/-/media/ACR/Files/Practice-Parameters/QCT.pdf?la. Accessed 30. May 2022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rozental TD, Makhni EC, Day CS et al (2008) Improving evaluation and treatment for osteoporosis following distal radial fractures. A prospective randomized intervention. J Bone Joint Surg Am 90(5):953–961. https://doi.org/10.2106/JBJS.G.01121

    Article  PubMed  Google Scholar 

  14. Lang TF, Guglielmi G, van Kuijk C et al (2002) Measurement of bone mineral density at the spine and proximal femur by volumetric quantitative computed tomography and dual-energy x‑ray absorptiometry in elderly women with and without vertebral fractures. Bone 30(1):247–250. https://doi.org/10.1016/s8756-3282(01)00647-0

    Article  CAS  PubMed  Google Scholar 

  15. Jing S, Li X, Zhang L et al (2011) Comparison of BMD value measured by QCT and DXA in patients with multiple myeloma. Chin J Med Imaging 19(12):887–889

    Google Scholar 

  16. Pickhardt PJ, Bodeen G, Brett A et al (2015) Comparison of femoral neck BMD value evaluation obtained using lunar DXA and QCT with asynchronous calibration from CT Colonography. J Clin Densitom 18(1):5–12. https://doi.org/10.1016/j.jocd.2014.03.002

    Article  PubMed  Google Scholar 

  17. Gausden EB, Nwachukwu BU, Schreiber JJ et al (2017) Opportunistic use of CT imaging for osteoporosis screening and bone density assessment: A qualitative systematic review. J Bone Joint Surg Am 99(18):1580–1590. https://doi.org/10.2106/JBJS.16.00749

    Article  PubMed  Google Scholar 

  18. Hendrickson NR, Pickhardt PJ, Del Rio AM et al (2018) Bone mineral density T‑scores derived from CT attenuation numbers (Hounsfield units): clinical utility and correlation with dual-energy X‑ray absorptiometry. Iowa Orthop J 38:25–31

    PubMed  PubMed Central  Google Scholar 

  19. Schreiber JJ, Anderson PA, Rosas HG et al (2011) Hounsfield units for assessing bone mineral density and strength: a tool for osteoporosis management. J Bone Joint Surg Am 93(11):1057–1063. https://doi.org/10.2106/JBJS.J.00160

    Article  PubMed  Google Scholar 

  20. Nappo KE, Christensen DL, Wolfe JA et al (2018) Glenoid neck Hounsfield units on computed tomography can accurately identify patients with low bone mineral density. J Shoulder Elbow Surg 27(7):1268–1274. https://doi.org/10.1016/j.jse.2017.11.008

    Article  PubMed  Google Scholar 

  21. Brenner DJ, Doll R, Goodhead DT et al (2003) Cancer risks attributable to low doses of ionizing radiation: assessing what we really know. Proc Natl Acad Sci U S A 100(24):13761–13766. https://doi.org/10.1073/pnas.2235592100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Petley GW, Taylor PA, Murrills AJ et al (2000) An Investigation of the diagnostic value of bilateral femoral neck bone mineral density measurements. Osteoporos Int 11(8):675–679. https://doi.org/10.1007/s001980070065

    Article  CAS  PubMed  Google Scholar 

  23. Johannesdottir F, Turmezei T, Poole KE (2014) Cortical bone assessed with clinical computed tomography at the proximal femur. J Bone Miner Res 29(4):771–783. https://doi.org/10.1002/jbmr.2199

    Article  PubMed  Google Scholar 

  24. Lee SY, Kwon SS, Kim HS et al (2015) Reliability and validity of lower extremity computed tomography as a screening tool for osteoporosis. Osteoporos Int 26(4):1387–1394. https://doi.org/10.1007/s00198-014-3013-x

    Article  CAS  PubMed  Google Scholar 

  25. Kim KJ, Dong HK, Lee JI et al (2019) Hounsfield units on lumbar computed tomography for predicting regional bone mineral density. Open Med (wars) 14:545–551. https://doi.org/10.1515/med-2019-0061

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

Not applicable.

Availability of data and materials

The data in this study are available on request from the corresponding author.

The data are not publicly available due to privacy or ethical restrictions.

Author information

Authors and Affiliations

  1. Department of Radiology, The First Hospital of Hebei Medical University, 050031, Shijiazhuang, Hebei Province, China

    Junlu Zhao, Zhai Liu, Qingyun Ren & Deyuan Zhao

  2. Operating room, The First Hospital Hebei Medical University, Shijiazhuang, Hebei Province, China

    Guanwei Nie

Authors
  1. Junlu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingyun Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guanwei Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Deyuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Junlu Zhao contributed to methodology and wrote the original draft; Zhai Liu contributed to data curation, resources, and visualization of the experiment results; Qingyun Ren contributed to conceptualization and supervision, helped data curation, co-wrote the original draft, and reviewed and edited the manuscript.

Guanwei NIE was responsible for supervision and investigation; Deyuan Zhao contributed to the conception of the study and data curation.

Corresponding author

Correspondence to Qingyun Ren.

Ethics declarations

Conflict of interest

J. Zhao, Z. Liu, Q. Ren, G. Nie, and D. Zhao declare that they have no competing interests.

The supplement containing this article is not sponsored by industry.

All procedures performed that involved human participants were in accordance with the ethical standards of the institutional and/or national research committee and approved by the Human Ethics Committee of the First Hospital of Hebei Medical University. The present study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Consent for publication: not applicable.

Additional information

figure qr

Scan QR code & read article online

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, J., Liu, Z., Ren, Q. et al. Measurement of Hounsfield units on proximal femur computed tomography for predicting regional osteoporosis. Radiologie 63 (Suppl 2), 90–97 (2023). https://doi.org/10.1007/s00117-023-01190-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00117-023-01190-z

Keywords

Schlüsselwörter

Search

Navigation