Skip to main content
Top
Published in: European Radiology 11/2018

Open Access 01-11-2018 | Musculoskeletal

Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study

Authors: James W. MacKay, Geeta Kapoor, Jeffrey B. Driban, Grace H. Lo, Timothy E. McAlindon, Andoni P. Toms, Andrew W. McCaskie, Fiona J. Gilbert

Published in: European Radiology | Issue 11/2018

Login to get access

Abstract

Objectives

To assess whether initial or 12–18-month change in magnetic resonance imaging (MRI) subchondral bone texture is predictive of radiographic knee osteoarthritis (OA) progression over 36 months.

Methods

This was a nested case-control study including 122 knees/122 participants in the Osteoarthritis Initiative (OAI) Bone Ancillary Study, who underwent MRI optimised for subchondral bone assessment at either the 30- or 36-month and 48-month OAI visits. Case knees (n = 61) had radiographic OA progression between the 36- and 72-month OAI visits, defined as ≥ 0.7 mm minimum medial tibiofemoral radiographic joint space (minJSW) loss. Control knees (n = 61) without radiographic OA progression were matched (1:1) to cases for age, sex, body mass index and initial medial minJSW. Texture analysis was performed on the medial femoral and tibial subchondral bone. We assessed the association of texture features with radiographic progression by creating a composite texture score using penalised logistic regression and calculating odds ratios. We evaluated the predictive performance of texture features for predicting radiographic progression using c-statistics.

Results

Initial (odds ratio [95% confidence interval] = 2.13 [1.41–3.40]) and 12– 18-month change (3.76 [2.04–7.82]) texture scores were significantly associated with radiographic OA progression. Combinations of texture features were significant predictors of radiographic progression using initial (c-statistic [95% confidence interval] = 0.65 [0.64–0.65], p = 0.003) and 12–18-month change (0.68 [0.68-0.68], p < 0.001) data.

Conclusions

Initial and 12–18-month changes in MRI subchondral bone texture score were significantly associated with radiographic progression at 36 months, with better predictive performance for 12–18-month change in texture. These results suggest that texture analysis may be a useful biomarker of subchondral bone in OA.

Key Points

• Subchondral bone MRI texture analysis is a promising knee osteoarthritis imaging biomarker.
• In this study, subchondral bone texture was associated with knee osteoarthritis progression.
• This demonstrates predictive and concurrent validity of MRI subchondral bone texture analysis.
• This method may be useful in clinical trials with interventions targeting bone.
Appendix
Available only for authorised users
Literature
1.
go back to reference Goldring MB, Goldring SR (2010) Articular cartilage and subchondral bone in the pathogenesis of osteoarthritis. Ann N Y Acad Sci 1192:230–237CrossRef Goldring MB, Goldring SR (2010) Articular cartilage and subchondral bone in the pathogenesis of osteoarthritis. Ann N Y Acad Sci 1192:230–237CrossRef
2.
go back to reference Kwan Tat S, Lajeunesse D, Pelletier J-P, Martel-Pelletier J (2010) Targeting subchondral bone for treating osteoarthritis: what is the evidence? Best Pract Res Clin Rheumatol 24:51–70CrossRef Kwan Tat S, Lajeunesse D, Pelletier J-P, Martel-Pelletier J (2010) Targeting subchondral bone for treating osteoarthritis: what is the evidence? Best Pract Res Clin Rheumatol 24:51–70CrossRef
3.
go back to reference Chang G, Xia D, Chen C et al (2015) 7T MRI detects deterioration in subchondral bone microarchitecture in subjects with mild knee osteoarthritis as compared with healthy controls. J Magn Reson Imaging 41:1311–1317CrossRef Chang G, Xia D, Chen C et al (2015) 7T MRI detects deterioration in subchondral bone microarchitecture in subjects with mild knee osteoarthritis as compared with healthy controls. J Magn Reson Imaging 41:1311–1317CrossRef
4.
go back to reference Kraus VB, Feng S, Wang S et al (2013) Subchondral Bone Trabecular Integrity Predicts and Changes Concurrently with Radiographic and Magnetic Resonance Imaging-Determined Knee Osteoarthritis Progression. Arthritis Rheum 65:1812–1821CrossRef Kraus VB, Feng S, Wang S et al (2013) Subchondral Bone Trabecular Integrity Predicts and Changes Concurrently with Radiographic and Magnetic Resonance Imaging-Determined Knee Osteoarthritis Progression. Arthritis Rheum 65:1812–1821CrossRef
7.
go back to reference Lo GH, Tassinari AM, Driban JB et al (2012) Cross-sectional DXA and MR measures of tibial periarticular bone associate with radiographic knee osteoarthritis severity. Osteoarthritis Cartilage 20:686–693CrossRef Lo GH, Tassinari AM, Driban JB et al (2012) Cross-sectional DXA and MR measures of tibial periarticular bone associate with radiographic knee osteoarthritis severity. Osteoarthritis Cartilage 20:686–693CrossRef
8.
go back to reference Hirvasniemi J, Thevenot J, Guermazi A et al (2017) Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study. Eur Radiol 27:4874–4882CrossRef Hirvasniemi J, Thevenot J, Guermazi A et al (2017) Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions - the Oulu Knee Osteoarthritis study. Eur Radiol 27:4874–4882CrossRef
9.
go back to reference MacKay JW, Murray PJ, Kasmai B et al (2016) MRI texture analysis of subchondral bone at the tibial plateau. Eur Radiol 26:3034–3045CrossRef MacKay JW, Murray PJ, Kasmai B et al (2016) MRI texture analysis of subchondral bone at the tibial plateau. Eur Radiol 26:3034–3045CrossRef
10.
go back to reference MacKay JW, Murray PJ, Low SBL et al (2016) Quantitative analysis of tibial subchondral bone: Texture analysis outperforms conventional trabecular microarchitecture analysis. J Magn Reson Imaging 43:1159–1170CrossRef MacKay JW, Murray PJ, Low SBL et al (2016) Quantitative analysis of tibial subchondral bone: Texture analysis outperforms conventional trabecular microarchitecture analysis. J Magn Reson Imaging 43:1159–1170CrossRef
11.
go back to reference MacKay JW, Murray PJ, Kasmai B et al (2017) Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry. Osteoarthritis Cartilage 25:700–707CrossRef MacKay JW, Murray PJ, Kasmai B et al (2017) Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry. Osteoarthritis Cartilage 25:700–707CrossRef
12.
go back to reference Hunter DJ, Nevitt M, Losina E, Kraus V (2014) Biomarkers for osteoarthritis: Current position and steps towards further validation. Best Pract Res Clin Rheumatol 28:61–71CrossRef Hunter DJ, Nevitt M, Losina E, Kraus V (2014) Biomarkers for osteoarthritis: Current position and steps towards further validation. Best Pract Res Clin Rheumatol 28:61–71CrossRef
13.
go back to reference Nevitt MC, Peterfy C, Guermazi A et al (2007) Longitudinal performance evaluation and validation of fixed-flexion radiography of the knee for detection of joint space loss. Arthritis Rheum 56:1512–1520CrossRef Nevitt MC, Peterfy C, Guermazi A et al (2007) Longitudinal performance evaluation and validation of fixed-flexion radiography of the knee for detection of joint space loss. Arthritis Rheum 56:1512–1520CrossRef
14.
go back to reference Duryea J, Neumann G, Niu J et al (2010) Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: analysis of longitudinal data from the Osteoarthritis Initiative. Arthritis Care Res 62:932–937CrossRef Duryea J, Neumann G, Niu J et al (2010) Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: analysis of longitudinal data from the Osteoarthritis Initiative. Arthritis Care Res 62:932–937CrossRef
15.
go back to reference Felson DT, Nevitt MC, Yang M et al (2008) A new approach yields high rates of radiographic progression in knee osteoarthritis. J Rheumatol 35:2047–2054PubMedPubMedCentral Felson DT, Nevitt MC, Yang M et al (2008) A new approach yields high rates of radiographic progression in knee osteoarthritis. J Rheumatol 35:2047–2054PubMedPubMedCentral
16.
go back to reference Iranpour-Boroujeni T, Li J, Lynch JA et al (2014) A new method to measure anatomic knee alignment for large studies of OA: data from the osteoarthritis initiative. Osteoarthritis Cartilage 22:1668–1674CrossRef Iranpour-Boroujeni T, Li J, Lynch JA et al (2014) A new method to measure anatomic knee alignment for large studies of OA: data from the osteoarthritis initiative. Osteoarthritis Cartilage 22:1668–1674CrossRef
17.
go back to reference Schneider E, Lo GH, Sloane G et al (2011) Magnetic resonance imaging evaluation of weight-bearing subchondral trabecular bone in the knee. Skeletal Radiol 40:95–103CrossRef Schneider E, Lo GH, Sloane G et al (2011) Magnetic resonance imaging evaluation of weight-bearing subchondral trabecular bone in the knee. Skeletal Radiol 40:95–103CrossRef
18.
go back to reference Szczypiński PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda--a software package for image texture analysis. Comput Methods Prog Biomed 94:66–76CrossRef Szczypiński PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda--a software package for image texture analysis. Comput Methods Prog Biomed 94:66–76CrossRef
19.
go back to reference Zou GY (2012) Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat Med 31:3972–3981CrossRef Zou GY (2012) Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat Med 31:3972–3981CrossRef
20.
go back to reference Ho D, Imai K, King G, Stuart EA (2011) MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw 42:1–28CrossRef Ho D, Imai K, King G, Stuart EA (2011) MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. J Stat Softw 42:1–28CrossRef
21.
go back to reference Friedman J, Hastie T, Tibshirani R (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33:1–22CrossRef Friedman J, Hastie T, Tibshirani R (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw 33:1–22CrossRef
23.
go back to reference Collins JE, Losina E, Nevitt MC et al (2016) Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheum 68:2422–2431CrossRef Collins JE, Losina E, Nevitt MC et al (2016) Semiquantitative Imaging Biomarkers of Knee Osteoarthritis Progression: Data From the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheum 68:2422–2431CrossRef
25.
go back to reference Eckstein F, Collins JE, Nevitt MC et al (2015) Brief Report: Cartilage Thickness Change as an Imaging Biomarker of Knee Osteoarthritis Progression: Data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheum 67:3184–3189CrossRef Eckstein F, Collins JE, Nevitt MC et al (2015) Brief Report: Cartilage Thickness Change as an Imaging Biomarker of Knee Osteoarthritis Progression: Data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheum 67:3184–3189CrossRef
26.
go back to reference Hunter D, Nevitt M, Lynch J et al (2016) Longitudinal validation of periarticular bone area and 3D shape as biomarkers for knee OA progression? Data from the FNIH OA Biomarkers Consortium. Ann Rheum Dis 75:1607–1614CrossRef Hunter D, Nevitt M, Lynch J et al (2016) Longitudinal validation of periarticular bone area and 3D shape as biomarkers for knee OA progression? Data from the FNIH OA Biomarkers Consortium. Ann Rheum Dis 75:1607–1614CrossRef
27.
go back to reference Hodgdon T, McInnes MDF, Schieda N et al (2015) Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology 276:787–796CrossRef Hodgdon T, McInnes MDF, Schieda N et al (2015) Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology 276:787–796CrossRef
28.
go back to reference Ng F, Ganeshan B, Kozarski R et al (2013) Assessment of Primary Colorectal Cancer Heterogeneity by Using Whole-Tumor Texture Analysis: Contrast-enhanced CT Texture as a Biomarker of 5-year Survival. Radiology 266:177–184CrossRef Ng F, Ganeshan B, Kozarski R et al (2013) Assessment of Primary Colorectal Cancer Heterogeneity by Using Whole-Tumor Texture Analysis: Contrast-enhanced CT Texture as a Biomarker of 5-year Survival. Radiology 266:177–184CrossRef
29.
go back to reference Rajani NK, Joshi NV, Elkhawad M et al (2014) CT textural analysis of abdominal aortic aneurysms as a biomarker for aneurysm growth. Lancet 383:S87CrossRef Rajani NK, Joshi NV, Elkhawad M et al (2014) CT textural analysis of abdominal aortic aneurysms as a biomarker for aneurysm growth. Lancet 383:S87CrossRef
Metadata
Title
Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study
Authors
James W. MacKay
Geeta Kapoor
Jeffrey B. Driban
Grace H. Lo
Timothy E. McAlindon
Andoni P. Toms
Andrew W. McCaskie
Fiona J. Gilbert
Publication date
01-11-2018
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5444-9

Other articles of this Issue 11/2018

European Radiology 11/2018 Go to the issue