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Erschienen in: Skeletal Radiology 11/2017

06.08.2017 | Scientific Article

Normative values for CT-based texture analysis of vertebral bodies in dual X-ray absorptiometry-confirmed, normally mineralized subjects

verfasst von: Manoj Mannil, Matthias Eberhard, Anton S. Becker, Denise Schönenberg, Georg Osterhoff, Diana P. Frey, Ender Konukoglu, Hatem Alkadhi, Roman Guggenberger

Erschienen in: Skeletal Radiology | Ausgabe 11/2017

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Abstract

Objectives

To develop age-, gender-, and regional-specific normative values for texture analysis (TA) of spinal computed tomography (CT) in subjects with normal bone mineral density (BMD), as defined by dual X-ray absorptiometry (DXA), and to determine age-, gender-, and regional-specific differences.

Materials and methods

In this retrospective, IRB-approved study, TA was performed on sagittal CT bone images of the thoracic and lumbar spine using dedicated software (MaZda) in 141 individuals with normal DXA BMD findings. Numbers of female and male subjects were balanced in each of six age decades. Three hundred and five TA features were analyzed in thoracic and lumbar vertebrae using free-hand regions-of-interest. Intraclass correlation (ICC) coefficients were calculated for determining intra- and inter-observer agreement of each feature. Further dimension reduction was performed with correlation analyses.

Results

The TA features with an ICC < 0.81 indicating compromised intra- and inter-observer agreement and with Pearson correlation scores r > 0.8 with other features were excluded from further analysis for dimension reduction. From the remaining 31 texture features, a significant correlation with age was found for the features mean (r = −0.489, p < 0.001), variance (r = −0.681, p < 0.001), kurtosis (r = 0.273, p < 0.001), and WavEnLL_s4 (r = 0.273, p < 0.001). Significant differences were found between genders for various higher-level texture features (p < 0.001). Regional differences among the thoracic spine, thoracic–lumbar junction, and lumbar spine were found for most TA features (p < 0.021).

Conclusion

This study established normative values of TA features on CT images of the spine and showed age-, gender-, and regional-specific differences in individuals with normal BMD as defined by DXA.
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Literatur
1.
Zurück zum Zitat Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentral Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentral
2.
Zurück zum Zitat Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology. 1999;213(2):317–20.CrossRefPubMed Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology. 1999;213(2):317–20.CrossRefPubMed
3.
Zurück zum Zitat Kassner A, Liu F, Thornhill RE, Tomlinson G, Mikulis DJ. Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images. J Magn Reson Imaging. 2009;30(5):933–41.CrossRefPubMed Kassner A, Liu F, Thornhill RE, Tomlinson G, Mikulis DJ. Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images. J Magn Reson Imaging. 2009;30(5):933–41.CrossRefPubMed
4.
Zurück zum Zitat Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol. 2016;85(4):824–9.CrossRefPubMed Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol. 2016;85(4):824–9.CrossRefPubMed
5.
Zurück zum Zitat Yu V, Ruan D, Nguyen D, Kaprealian T, Chin R, Sheng K. SU-F-R-17: advancing glioblastoma multiforme (GBM) recurrence detection with MRI image texture feature extraction and machine learning. Fifty-eighth Annual Meeting of the American Association of Physicists in Medicine. Med Phys. 2016:3376–7. Yu V, Ruan D, Nguyen D, Kaprealian T, Chin R, Sheng K. SU-F-R-17: advancing glioblastoma multiforme (GBM) recurrence detection with MRI image texture feature extraction and machine learning. Fifty-eighth Annual Meeting of the American Association of Physicists in Medicine. Med Phys. 2016:3376–7.
6.
Zurück zum Zitat Ganeshan B, Miles KA, Young RC, Chatwin CR. Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol. 2009;70(1):101–10.CrossRefPubMed Ganeshan B, Miles KA, Young RC, Chatwin CR. Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol. 2009;70(1):101–10.CrossRefPubMed
7.
Zurück zum Zitat Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10:137–43.CrossRefPubMedPubMedCentral Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging. 2010;10:137–43.CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat Rachidi M, Marchadier A, Gadois C, Lespessailles E, Chappard C, Benhamou CL. Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis. Skeletal Radiol. 2008;37(6):541–8.CrossRefPubMed Rachidi M, Marchadier A, Gadois C, Lespessailles E, Chappard C, Benhamou CL. Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis. Skeletal Radiol. 2008;37(6):541–8.CrossRefPubMed
9.
Zurück zum Zitat MacKay JW, Murray PJ, Kasmai B, Johnson G, Donell ST, Toms AP. MRI texture analysis of subchondral bone at the tibial plateau. Eur Radiol. 2016;26(9):3034–45.CrossRefPubMed MacKay JW, Murray PJ, Kasmai B, Johnson G, Donell ST, Toms AP. MRI texture analysis of subchondral bone at the tibial plateau. Eur Radiol. 2016;26(9):3034–45.CrossRefPubMed
10.
Zurück zum Zitat MacKay JW, Murray PJ, Kasmai B, Johnson G, Donell ST, Toms AP. Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry. Osteoarthritis Cartilage. 2017;25(5):700–7.CrossRefPubMed MacKay JW, Murray PJ, Kasmai B, Johnson G, Donell ST, Toms AP. Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry. Osteoarthritis Cartilage. 2017;25(5):700–7.CrossRefPubMed
11.
Zurück zum Zitat MacKay JW, Murray PJ, Low SB, Kasmai B, Johnson G, Donell ST, et al. Quantitative analysis of tibial subchondral bone: texture analysis outperforms conventional trabecular microarchitecture analysis. J Magn Reson Imaging. 2016;43(5):1159–70.CrossRefPubMed MacKay JW, Murray PJ, Low SB, Kasmai B, Johnson G, Donell ST, et al. Quantitative analysis of tibial subchondral bone: texture analysis outperforms conventional trabecular microarchitecture analysis. J Magn Reson Imaging. 2016;43(5):1159–70.CrossRefPubMed
12.
Zurück zum Zitat Schneider E, Lo GH, Sloane G, Fanella L, Hunter DJ, Eaton CB, et al. Magnetic resonance imaging evaluation of weight-bearing subchondral trabecular bone in the knee. Skeletal Radiol. 2011;40(1):95–103.CrossRefPubMed Schneider E, Lo GH, Sloane G, Fanella L, Hunter DJ, Eaton CB, et al. Magnetic resonance imaging evaluation of weight-bearing subchondral trabecular bone in the knee. Skeletal Radiol. 2011;40(1):95–103.CrossRefPubMed
13.
Zurück zum Zitat Phan CM, Macklin EA, Bredella MA, Dadrich M, Flechsig P, Yoo AJ, et al. Trabecular structure analysis using C-arm CT: comparison with MDCT and flat-panel volume CT. Skeletal Radiol. 2011;40(8):1065–72.CrossRefPubMed Phan CM, Macklin EA, Bredella MA, Dadrich M, Flechsig P, Yoo AJ, et al. Trabecular structure analysis using C-arm CT: comparison with MDCT and flat-panel volume CT. Skeletal Radiol. 2011;40(8):1065–72.CrossRefPubMed
14.
Zurück zum Zitat Frighetto-Pereira L, Rangayyan RM, Metzner GA, de Azevedo-Marques PM, Nogueira-Barbosa MH. Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput Biol Med. 2016;73:147–56.CrossRefPubMed Frighetto-Pereira L, Rangayyan RM, Metzner GA, de Azevedo-Marques PM, Nogueira-Barbosa MH. Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput Biol Med. 2016;73:147–56.CrossRefPubMed
16.
Zurück zum Zitat Tabari A, Torriani M, Miller KK, Klibanski A, Kalra MK, Bredella MA. Anorexia nervosa: analysis of trabecular texture with CT. Radiology. 2017;283(1):178–85CrossRefPubMed Tabari A, Torriani M, Miller KK, Klibanski A, Kalra MK, Bredella MA. Anorexia nervosa: analysis of trabecular texture with CT. Radiology. 2017;283(1):178–85CrossRefPubMed
17.
Zurück zum Zitat Cosman F, Crittenden DB, Adachi JD, Binkley N, Czerwinski E, Ferrari S, et al. Romosozumab treatment in postmenopausal women with osteoporosis. N Engl J Med. 2016;375(16):1532–43.CrossRefPubMed Cosman F, Crittenden DB, Adachi JD, Binkley N, Czerwinski E, Ferrari S, et al. Romosozumab treatment in postmenopausal women with osteoporosis. N Engl J Med. 2016;375(16):1532–43.CrossRefPubMed
18.
Zurück zum Zitat Szczypinski PM, Strzelecki M, Materka A, Klepaczko A. MaZda—a software package for image texture analysis. Comput Methods Prog Biomed. 2009;94(1):66–76.CrossRef Szczypinski PM, Strzelecki M, Materka A, Klepaczko A. MaZda—a software package for image texture analysis. Comput Methods Prog Biomed. 2009;94(1):66–76.CrossRef
19.
Zurück zum Zitat Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22(1):81–91.CrossRefPubMed Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22(1):81–91.CrossRefPubMed
20.
Zurück zum Zitat Griffith JF, Yeung DK, Ma HT, Leung JC, Kwok TC, Leung PC. Bone marrow fat content in the elderly: a reversal of sex difference seen in younger subjects. J Magn Reson Imaging. 2012;36(1):225–30.CrossRefPubMed Griffith JF, Yeung DK, Ma HT, Leung JC, Kwok TC, Leung PC. Bone marrow fat content in the elderly: a reversal of sex difference seen in younger subjects. J Magn Reson Imaging. 2012;36(1):225–30.CrossRefPubMed
21.
Zurück zum Zitat Mahato NK. Trabecular bone structure in lumbosacral transitional vertebrae: distribution and densities across sagittal vertebral body segments. Spine J. 2013;13(8):932–7.CrossRefPubMed Mahato NK. Trabecular bone structure in lumbosacral transitional vertebrae: distribution and densities across sagittal vertebral body segments. Spine J. 2013;13(8):932–7.CrossRefPubMed
22.
Zurück zum Zitat Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74.CrossRefPubMed Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74.CrossRefPubMed
23.
Zurück zum Zitat Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol. 2014;21(12):1587–96.CrossRefPubMedPubMedCentral Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol. 2014;21(12):1587–96.CrossRefPubMedPubMedCentral
24.
Zurück zum Zitat Potdevin G, Malecki A, Biernath T, Bech M, Jensen TH, Feidenhans’l R, et al. X-ray vector radiography for bone micro-architecture diagnostics. Phys Med Biol. 2012;57(11):3451–61.CrossRefPubMed Potdevin G, Malecki A, Biernath T, Bech M, Jensen TH, Feidenhans’l R, et al. X-ray vector radiography for bone micro-architecture diagnostics. Phys Med Biol. 2012;57(11):3451–61.CrossRefPubMed
25.
Zurück zum Zitat Leboeuf-Yde C, Nielsen J, Kyvik KO, Fejer R, Hartvigsen J. Pain in the lumbar, thoracic or cervical regions: do age and gender matter? A population-based study of 34,902 Danish twins 20-71 years of age. BMC Musculoskelet Disord. 2009;10:39.CrossRefPubMedPubMedCentral Leboeuf-Yde C, Nielsen J, Kyvik KO, Fejer R, Hartvigsen J. Pain in the lumbar, thoracic or cervical regions: do age and gender matter? A population-based study of 34,902 Danish twins 20-71 years of age. BMC Musculoskelet Disord. 2009;10:39.CrossRefPubMedPubMedCentral
26.
Zurück zum Zitat Yeni YN, Zinno MJ, Yerramshetty JS, Zauel R, Fyhrie DP. Variability of trabecular microstructure is age-, gender-, race- and anatomic site-dependent and affects stiffness and stress distribution properties of human vertebral cancellous bone. Bone. 2011;49(4):886–94.CrossRefPubMedPubMedCentral Yeni YN, Zinno MJ, Yerramshetty JS, Zauel R, Fyhrie DP. Variability of trabecular microstructure is age-, gender-, race- and anatomic site-dependent and affects stiffness and stress distribution properties of human vertebral cancellous bone. Bone. 2011;49(4):886–94.CrossRefPubMedPubMedCentral
27.
Zurück zum Zitat Terashima Y, Yurube T, Hirata H, Sugiyama D, Sumi M, Hyogo Organization of Spinal Disorders. Predictive risk factors of cervical spine instabilities in rheumatoid arthritis: a prospective multicenter over 10-year cohort study. Spine. 2017; 42(8):556–64. Terashima Y, Yurube T, Hirata H, Sugiyama D, Sumi M, Hyogo Organization of Spinal Disorders. Predictive risk factors of cervical spine instabilities in rheumatoid arthritis: a prospective multicenter over 10-year cohort study. Spine. 2017; 42(8):556–64.
Metadaten
Titel
Normative values for CT-based texture analysis of vertebral bodies in dual X-ray absorptiometry-confirmed, normally mineralized subjects
verfasst von
Manoj Mannil
Matthias Eberhard
Anton S. Becker
Denise Schönenberg
Georg Osterhoff
Diana P. Frey
Ender Konukoglu
Hatem Alkadhi
Roman Guggenberger
Publikationsdatum
06.08.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology / Ausgabe 11/2017
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-017-2728-0

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