European Radiology

, Volume 29, Issue 4, pp 1831–1840 | Cite as

Comparison of the diagnostic performance of CT Hounsfield unit histogram analysis and dual-energy X-ray absorptiometry in predicting osteoporosis of the femur

  • Hyun Kyung Lim
  • Hong Il HaEmail author
  • Sun-Young Park
  • Kwanseop Lee



To evaluate the diagnostic performance of Hounsfield unit histogram analysis (HUHA) of precontrast abdominal-pelvic CT scans for predicting osteoporosis.

Materials and methods

The study included 271 patients who had undergone dual X-ray absorptiometry (DXA) and abdominal-pelvic CT within 1 month. HUHA was measured using commercial 3D analysis software (Aquarius iNtuition v4.4.12, TeraRecon) and expressed as a percentage of seven HU range categories related to the ROI: A < 0, 0 ≤ B < 25, 25 ≤ C < 50, 50 ≤ D < 75, 75 ≤ E < 100, 100 ≤ F < 130, and 130 ≤ G. A coronal reformatted precontrast CT image containing the largest Ward’s triangle was selected and then the ROI was drawn over the femoral neck. Correlation (r) and ROC curve analyses were used to assess diagnostic performance in predicting osteoporosis using the femur T-score as the reference standard.


When the femur T-score was used as the reference, the rs of HUHA-A and HUHA-G were 0.74 and -0.57, respectively. Other HUHA values had moderate to weak correlations (r = -0.33 to 0.27). The correlation of HUHA-A was significantly higher than that of HUHA-G (p = 0.03). The area under the curve (0.95) of HUHA-A differed significantly from that of HUHA-G (0.90; p < 0.01). A HUHA-A threshold ≥ 27.7% was shown to predict osteoporosis based on a sensitivity and specificity of 95.6% and 81.7%, respectively.


The HUHA-A value of the femoral neck is closely related to osteoporosis and may help predict osteoporosis.

Key Points

• HUHA correlated strongly with the DXA femur T-score (HUHA-A, r = 0.74).

• The diagnostic performance of HUHA for predicting osteoporosis (AUC = 0.95) was better than that of the average CT HU value (AUC = 0.91; p < 0.05).

• HUHA may help predict osteoporosis and enable semi-quantitative measurement of changes in bone mineral density.


Diagnosis, Computer-assisted Osteoporosis Tomography, X-ray computed Absorptiometry, Photon 



Automatic tube current modulation


Automatic tube voltage selection


Bone mineral density


Confidence interval


Dual-energy X-ray absorptiometry


Hounsfield unit


Hounsfield unit histogram analysis


Intra-class correlation coefficient



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Kwanseop Lee.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution


  1. 1.
    Sambrook P, Cooper C (2006) Osteoporosis. Lancet 367:2010–2018CrossRefPubMedGoogle Scholar
  2. 2.
    Riggs BL, Melton LJ 3rd (1995) The worldwide problem of osteoporosis: Insights afforded by epidemiology. Bone 17:505S–511SGoogle Scholar
  3. 3.
    Office of the Surgeon General (US) (2004) Bone Health and Osteoporosis: A Report of the Surgeon General. Office of the Surgeon General (US), Rockville (MD) Available from: Accessed 17 Aug 2018Google Scholar
  4. 4.
    Johnell O, Kanis JA, Odén A et al (2004) Mortality after osteoporotic fractures. Osteoporos Int 15:38–42CrossRefPubMedGoogle Scholar
  5. 5.
    Lewiecki EM, Gordon CM, Baim S et al (2008) International Society for Clinical Densitometry 2007 adult and pediatric official positions. Bone 43:1115–1121CrossRefPubMedGoogle Scholar
  6. 6.
    Nelson HD, Haney EM, Dana T, Bougatsos C, Chou R (2010) Screening for osteoporosis: An update for the US. Preventive Services Task Force. Ann Intern Med 153:99–111CrossRefPubMedGoogle Scholar
  7. 7.
    Amarnath AL, Franks P, Robbins JA, Xing G, Fenton JJ (2015) Underuse and overuse of osteoporosis screening in a regional health system: a retrospective cohort study. J Gen Intern Med 30:1733–1740CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gillespie CW, Morin PE (2017) Osteoporosis-related health services utilization following first hip fracture among a cohort of privately-insured women in the United States, 2008-2014: an observational study. J Bone Miner Res 32:1052–1061CrossRefPubMedGoogle Scholar
  9. 9.
    van Hamersvelt RW, Schilham AM, Engelke K et al (2017) Accuracy of bone mineral density quantification using dual-layer spectral detector CT: A phantom study. Eur Radiol 27:4351–4359CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Pompe E, de Jong PA, de Jong WU et al (2016) Inter-observer and inter-examination variability of manual vertebral bone attenuation measurement on computed tomography. Eur Radiol 26:3046–3053CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Emohare O, Cagan A, Morgan R et al (2014) The use of computed tomography attenuation to evaluate osteoporosis following acute fractures of the thoracic and lumbar vertebra. Geriatr Orthop Surg Rehabil 5:50–55CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Tay WL, Chui CK, Ong SH, Ng AC (2012) Osteoporosis screening using areal bone mineral density estimation from diagnostic CT images. Acad Radiol 19:1273–1282CrossRefPubMedGoogle Scholar
  13. 13.
    Pickhardt PJ, Lee LJ, del Rio AM et al (2011) Simultaneous screening for osteoporosis at CT colonography: Bone mineral density assessment using MDCT attenuation techniques compared with the DXA reference standard. J Bone Miner Res 26:2194–2203CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Mueller DK, Kutscherenko A, Bartel H, Vlassenbroek A, Ourednicek P, Erckenbrecht J (2011) Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation. Eur J Radiol 79:375–381CrossRefPubMedGoogle Scholar
  15. 15.
    Buckens CF, Dijkhuis G, de Keizer B, Verhaar HJ, de Jong PA (2015) Opportunistic screening for osteoporosis on routine computed tomography? An external validation study. Eur Radiol 25:2074–2079CrossRefPubMedGoogle Scholar
  16. 16.
    Liu G, Peacock M, Eilam O, Dorulla G, Braunstein E, Johnston CC (1997) Effect of osteoarthritis in the lumbar spine and hip on bone mineral density and diagnosis of osteoporosis in elderly men and women. Osteoporos Int 7:564–569CrossRefPubMedGoogle Scholar
  17. 17.
    Poole KES, Skingle L, Gee AH et al (2017) Focal osteoporosis defects play a key role in hip fracture. Bone 94:124–134CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Fujii M, Aoki T, Okada Y et al (2016) Prediction of femoral neck strength in patients with diabetes mellitus with trabecular bone analysis and tomosynthesis images. Radiology 281:933–939CrossRefPubMedGoogle Scholar
  19. 19.
    Kanis JA (2002) Diagnosis of osteoporosis and assessment of fracture risk. Lancet 359:1929–1936CrossRefPubMedGoogle Scholar
  20. 20.
    Johnell O, Kanis JA, Odén A et al (2005) Predictive value of BMD for hip and other fractures. J Bone Miner Res 20:1185–1194CrossRefPubMedGoogle Scholar
  21. 21.
    Pompe E, Willemink MJ, Dijkhuis GR, Verhaar HJ, Mohamed Hoesein FA, de Jong PA (2015) Intravenous contrast injection significantly affects bone mineral density measured on CT. Eur Radiol 25:283–289CrossRefPubMedGoogle Scholar
  22. 22.
    Shrout PE, Fleiss JL (1979) Intraclass correlations: Uses in assessing rater reliability. Psychol Bull 86:420–428CrossRefPubMedGoogle Scholar
  23. 23.
    Büsing KA, Kilian AK, Schaible T, Debus A, Weiss C, Neff KW (2008) Reliability and validity of MR image lung volume measurement in fetuses with congenital diaphragmatic hernia and in vitro lung models. Radiology 246:553–561CrossRefPubMedGoogle Scholar
  24. 24.
    Landis JR, Koch GG (1977) An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33:363–374CrossRefPubMedGoogle Scholar
  25. 25.
    Chen H, Zhou X, Fujita H, Onozuka M, Kubo KY (2013) Age-related changes in trabecular and cortical bone microstructure. Int J Endocrinol 2013:213234Google Scholar
  26. 26.
    Sundh D, Rudang R, Zoulakis M, Nilsson AG, Darelid A, Lorentzon M (2016) A high amount of local adipose tissue is associated with high cortical porosity and low bone material strength in older women. J Bone Miner Res 31:749–757CrossRefPubMedGoogle Scholar
  27. 27.
    Yu W, Glüer CC, Fuerst T et al (1995) Influence of degenerative joint disease on spinal bone mineral measurements in postmenopausal women. Calcif Tissue Int 57:169–174CrossRefPubMedGoogle Scholar
  28. 28.
    Gruber M, Bauer JS, Dobritz M et al (2013) Bone mineral density measurements of the proximal femur from routine contrast-enhanced MDCT data sets correlate with dual-energy X-ray absorptiometry. Eur Radiol 23:505–512CrossRefPubMedGoogle Scholar
  29. 29.
    Link TM (2012) Osteoporosis imaging: State of the art and advanced imaging. Radiology 263:3–17CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Mei K, Kopp FK, Bippus R et al (2017) Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling? Eur Radiol 27:5261–5271CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Wichmann JL, Booz C, Wesarg S et al (2015) Quantitative dual-energy CT for phantomless evaluation of cancellous bone mineral density of the vertebral pedicle: Correlation with pedicle screw pull-out strength. Eur Radiol 25:1714–1720CrossRefPubMedGoogle Scholar
  32. 32.
    Wichmann JL, Booz C, Wesarg S et al (2014) Dual-energy CT-based phantomless in vivo three-dimensional bone mineral density assessment of the lumbar spine. Radiology 271:778–784CrossRefPubMedGoogle Scholar
  33. 33.
    Booz C, Hofmann PC, Sedlmair M et al (2017) Evaluation of bone mineral density of the lumbar spine using a novel phantomless dual-energy CT post-processing algorithm in comparison with dual-energy X-ray absorptiometry. Eur Radiol Exp 1:11. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    US Preventive Services Task Force (2011) Screening for osteoporosis: U.S. preventive services task force recommendation statement. Ann Intern Med 154:356–364Google Scholar
  35. 35.
    Lim LS, Hoeksema LJ, Sherin K, ACPM Prevention Practice Committee (2009) Screening for osteoporosis in the adult U.S. population: ACPM position statement on preventive practice. Am J Prev Med 36:366–375CrossRefPubMedGoogle Scholar
  36. 36.
    Crandall CJ, Larson J, Gourlay ML et al (2014) Osteoporosis screening in postmenopausal women 50 to 64 years old: comparison of US Preventive Services Task Force strategy and two traditional strategies in the Women's Health Initiative. J Bone Miner Res 29:1661–1666CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19:385–397Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologySoonchunhyang University Seoul HospitalSeoulRepublic of Korea
  2. 2.Department of RadiologyHallym University Sacred Heart HospitalAnyang-siRepublic of Korea

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