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Comparison of HU histogram analysis and BMD for proximal femoral fragility fracture assessment: a retrospective single-center case–control study

  • Musculoskeletal
  • Published:
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Abstract

Objectives

To evaluate the feasibility of HU histogram analysis (HUHA) to assess proximal femoral fragility fractures with respect to BMD.

Methods

This retrospective study included 137 patients with femoral fragility fractures who underwent hip CT and 137 control patients without fractures who underwent abdominal CT between January 2018 and February 2019. HUHA was calculated with the 3D volume of interest from the femoral head to the lesser trochanter. HUHAfat (percentage of negative HU values) and HUHAbone (percentage of HU values ≥ 125 HU) were assumed to be fat and bone components, respectively. Statistical significance was assessed using the area under the receiver operating characteristic curve (AUC), Spearman correlation (ρ), and odds ratio.

Results

HUHAfat was strongly positively correlated (ρ = 0.56) and BMD was moderately negatively correlated with fragility fractures (ρ =  − 0.37). AUC of HUHAfat (0.82, 95% CI [0.77, 0.87]) significantly differed from that of BMD (0.69, 95% CI [0.63, 0.75]) (p < .001). The cutoff value was 15.8% for HUHAfat (sensitivity: 90.4%; specificity: 67.7%) and 0.709 g/cm2 for BMD (sensitivity: 87.5%; specificity: 51.5%), with higher HUHAfat and lower BMD values indicating fragility fractures. The odds ratio of HUHAfat was 19.5 (95% CI [9.9, 38.2], p < .001), which was higher than that of BMD, 7.4 (95% CI [4.0, 13.6], p < .001).

Conclusion

HUHAfat revealed better performance than BMD and demonstrated feasibility in assessing proximal femoral fragility fractures.

Key Points

• HUHA fat showed a strong positive correlation (Spearman ρ = 0.56, p < .001), and BMD showed a moderate negative correlation (Spearman ρ =  − 0.37, p < .001) with proximal femoral fragility fractures.

• HUHA fat (AUC = 0.82) performed significantly better than BMD in assessing proximal femoral fragility fractures (AUC = 0.69) (p < .001).

• The odds ratio of HUHA fat for proximal femoral fragility fractures was higher than that of BMD (19.5 and 7.4, respectively; p < .001).

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Abbreviations

BMD:

Bone mineral density

DXA:

Dual-energy X-ray absorptiometry

HU:

Hounsfield unit

References

  1. Burge R, Dawson-Hughes B, Solomon DH, Wong JB, King A, Tosteson A (2007) Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res 22(3):465–475

    Article  PubMed  Google Scholar 

  2. Metcalfe D (2008) The pathophysiology of osteoporotic hip fracture. Mcgill J Med 11(1):51–57

    PubMed  PubMed Central  Google Scholar 

  3. Kani KK, Porrino JA, Mulcahy H, Chew FS (2019) Fragility fractures of the proximal femur: review and update for radiologists. Skeletal Radiol 48(1):29–45

    Article  PubMed  Google Scholar 

  4. Dragomir-Daescu D, Op Den Buijs J, McEligot S et al (2011) Robust QCT/FEA models of proximal femur stiffness and fracture load during a sideways fall on the hip. Ann Biomed Eng 39(2):742–755

    Article  PubMed  Google Scholar 

  5. 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(3):933–939

    Article  PubMed  Google Scholar 

  6. Poole KES, Skingle L, Gee AH et al (2017) Focal osteoporosis defects play a key role in hip fracture. Bone 94:124–134

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hong W, Ha HI, Lee JW, Lee SM, Kim MJ (2019) Measurement of pancreatic fat fraction by CT histogram analysis to predict pancreatic fistula after pancreaticoduodenectomy. Korean J Radiol 20(4):599–608

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lim HK, Ha HI, Park SY, Lee K (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

    Article  PubMed  Google Scholar 

  9. Lee HW, Ha HI, Park SY, Lim HK (2020) Reliability of 3D image analysis and influence of contrast medium administration on measurement of Hounsfield unit values of the proximal femur. PLoS One 15(10):e0241012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rivadeneira F, Zillikens MC, De Laet CE et al (2007) Femoral neck BMD is a strong predictor of hip fracture susceptibility in elderly men and women because it detects cortical bone instability: the Rotterdam Study. J Bone Miner Res 22(11):1781–1790

    Article  PubMed  Google Scholar 

  11. Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312(7041):1254–1259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cummings SR, Black DM, Nevitt MC et al (1993) Bone density at various sites for prediction of hip fractures. The Study of Osteoporotic Fractures Research Group. Lancet 341(8837):72–75

    Article  CAS  PubMed  Google Scholar 

  13. Kanis JA, Oden A, Johnell O, Jonsson B, de Laet C, Dawson A (2001) The burden of osteoporotic fractures: a method for setting intervention thresholds. Osteoporos Int 12(5):417–427

    Article  CAS  PubMed  Google Scholar 

  14. 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(2):283–289

  15. Schwartz AV, Sigurdsson S, Hue TF et al (2013) Vertebral bone marrow fat associated with lower trabecular BMD and prevalent vertebral fracture in older adults. J Clin Endocrinol Metab 98(6):2294–2300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wehrli FW, Hopkins JA, Hwang SN, Song HK, Snyder PJ, Haddad JG (2000) Cross-sectional study of osteopenia with quantitative MR imaging and bone densitometry. Radiology 217(2):527–538

    Article  CAS  PubMed  Google Scholar 

  17. Fazeli PK, Horowitz MC, MacDougald OA et al (2013) Marrow fat and bone-new perspectives. J Clin Endocrinol Metab 98(3):935–945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lim HK, Ha HI, Park SY, Han J (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):e0247330

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fraser LA, Langsetmo L, Berger C et al (2011) Fracture prediction and calibration of a Canadian FRAX(R) tool: a population-based report from CaMos. Osteoporos Int 22(3):829–837

    Article  PubMed  Google Scholar 

  20. Wainwright SA, Marshall LM, Ensrud KE et al (2005) Hip fracture in women without osteoporosis. J Clin Endocrinol Metab 90(5):2787–2793

    Article  CAS  PubMed  Google Scholar 

  21. Kaptoge S, Benevolenskaya LI, Bhalla AK et al (2005) Low BMD is less predictive than reported falls for future limb fractures in women across Europe: results from the European Prospective Osteoporosis Study. Bone 36(3):387–398

    Article  CAS  PubMed  Google Scholar 

  22. Dragomir-Daescu D, Rossman TL, Rezaei A et al (2018) Factors associated with proximal femur fracture determined in a large cadaveric cohort. Bone 116:196–202

    Article  PubMed  PubMed Central  Google Scholar 

  23. Robbins JA, Schott AM, Garnero P, Delmas PD, Hans D, Meunier PJ (2005) Risk factors for hip fracture in women with high BMD: EPIDOS study. Osteoporos Int 16(2):149–154

    Article  CAS  PubMed  Google Scholar 

  24. 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(1):34–43

    Article  PubMed  Google Scholar 

  25. Kim H, Kim JH, Kim MJ et al (2020) Low predictive value of FRAX adjusted by trabecular bone score for osteoporotic fractures in korean women: a community-based cohort study. Endocrinol Metab (Seoul) 35(2):359–366

    Article  Google Scholar 

  26. Gonzalez-Macias J, Marin F, Vila J, Diez-Perez A (2012) Probability of fractures predicted by FRAX(R) and observed incidence in the Spanish ECOSAP Study cohort. Bone 50(1):373–377

    Article  PubMed  Google Scholar 

  27. 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(9):2194–2203

    Article  PubMed  Google Scholar 

  28. 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

    Article  CAS  PubMed  Google Scholar 

  29. Wormanns D, Kohl G, Klotz E et al (2004) Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 14(1):86–92

    Article  PubMed  Google Scholar 

  30. Preda L, Lovati E, Chiesa F et al (2007) Measurement by multidetector CT scan of the volume of hypopharyngeal and laryngeal tumours: accuracy and reproducibility. Eur Radiol 17(8):2096–2102

    Article  PubMed  Google Scholar 

  31. Lee J, Lee S, Jang S, Ryu OH (2013) Age-related changes in the prevalence of osteoporosis according to gender and skeletal site: the Korea national health and nutrition examination survey 2008–2010. Endocrinol Metab (Seoul) 28(3):180–191

    Article  Google Scholar 

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Acknowledgements

Junhee Han, Ph.D. (Professor of Department of Statistics and Data Science Convergence Research Center at Hallym University in the Republic of Korea), an expert in statistics, provided the advice of statistical analysis of this study.

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Authors and Affiliations

  1. Department of Radiology, Hallym University Sacred Heart Hospital, 22, Gwanpyeong-ro 170beon-gil, Dongan-gu, Anyang-si, Gyeonggi-do, Republic of Korea, 14068

    Sun-Young Park, Hong Il Ha & Injae Lee

  2. Department of Radiology, Soonchunhyang University Seoul Hospital, 59, Daesagwan-ro, Yongsan-gu, Seoul, Republic of Korea, 04401

    Hyun Kyung Lim

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  1. Sun-Young Park
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Correspondence to Hong Il Ha.

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The scientific guarantor of this publication is Injae Lee, Professor and Chair for department of radiology in Hallym Sacred Heart Hospital.

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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.

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Written informed consent was waived by the Institutional Review Board.

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• case–control study/cross sectional study

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Park, SY., Ha, H.I., Lee, I. et al. Comparison of HU histogram analysis and BMD for proximal femoral fragility fracture assessment: a retrospective single-center case–control study. Eur Radiol 32, 1448–1455 (2022). https://doi.org/10.1007/s00330-021-08281-2

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  • DOI: https://doi.org/10.1007/s00330-021-08281-2

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