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Semin Musculoskelet Radiol 2024; 28(01): 014-025
DOI: 10.1055/s-0043-1776432
Review Article

Imaging Biomarkers of Osteoarthritis

Frank W. Roemer 
1   Department of Radiology, Chobanian & Avedisian Boston University School of Medicine, Boston, Massachusetts
2   Department of Radiology, Universitätsklinikum Erlangen & Friedrich Alexander Universität (FAU) Erlangen–Nürnberg, Erlangen, Germany
,
Wolfgang Wirth 
3   Center of Anatomy, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
4   Chondrometrics, GmbH, Freilassing, Germany
,
Shadpour Demehri 
5   Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Richard Kijowski 
6   Department of Radiology, New York University Grossmann School of Medicine, New York, New York
,
Mohamed Jarraya
7   Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
,
Daichi Hayashi 
8   Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts
9   Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
,
Felix Eckstein 
3   Center of Anatomy, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
4   Chondrometrics, GmbH, Freilassing, Germany
,
Ali Guermazi 
1   Department of Radiology, Chobanian & Avedisian Boston University School of Medicine, Boston, Massachusetts
10   Department of Radiology, Boston VA Healthcare System, West Roxbury, Massachusetts
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Abstract

Currently no disease-modifying osteoarthritis drug has been approved for the treatment of osteoarthritis (OA) that can reverse, hold, or slow the progression of structural damage of OA-affected joints. The reasons for failure are manifold and include the heterogeneity of structural disease of the OA joint at trial inclusion, and the sensitivity of biomarkers used to measure a potential treatment effect.

This article discusses the role and potential of different imaging biomarkers in OA research. We review the current role of radiography, as well as advances in quantitative three-dimensional morphological cartilage assessment and semiquantitative whole-organ assessment of OA. Although magnetic resonance imaging has evolved as the leading imaging method in OA research, recent developments in computed tomography are also discussed briefly. Finally, we address the experience from the Foundation for the National Institutes of Health Biomarker Consortium biomarker qualification study and the future role of artificial intelligence.

Keywords

osteoarthritis - biomarker - magnetic resonance imaging - radiography - computed tomography


Publication History

Article published online:
08 February 2024

© 2024. Thieme. All rights reserved.

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  • References

  • 1 FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource. Silver Spring, MD: Food and Drug Administration; 2016
    Google Scholar
  • 2 Kraus VB, Nevitt M, Sandell LJ. Summary of the OA biomarkers workshop 2009—biochemical biomarkers: biology, validation, and clinical studies. Osteoarthritis Cartilage 2010; 18 (06) 742-745
    CrossrefPubMedGoogle Scholar
  • 3 Hunter DJ, Losina E, Guermazi A, Burstein D, Lassere MN, Kraus V. A pathway and approach to biomarker validation and qualification for osteoarthritis clinical trials. Curr Drug Targets 2010; 11 (05) 536-545
    CrossrefPubMedGoogle Scholar
  • 4 Oo WM, Little C, Duong V, Hunter DJ. The development of disease-modifying therapies for osteoarthritis (DMOADs): the evidence to date. Drug Des Devel Ther 2021; 15: 2921-2945
    CrossrefPubMedGoogle Scholar
  • 5 Hochberg MC, Guermazi A, Guehring H. et al. Effect of intra-articular sprifermin vs placebo on femorotibial joint cartilage thickness in patients with osteoarthritis: the FORWARD randomized clinical trial. JAMA 2019; 322 (14) 1360-1370
    CrossrefPubMedGoogle Scholar
  • 6 Conaghan PG, Bowes MA, Kingsbury SR. et al. Disease-modifying effects of a novel cathepsin K inhibitor in osteoarthritis: a randomized controlled trial. Ann Intern Med 2020; 172 (02) 86-95
    CrossrefPubMedGoogle Scholar
  • 7 Thomas D, Burns J, Audette J, Carroll A, Dow-Hygelund C, Hay M. Clinical development success rates 2006–2015: Biotechnology Innovation Organization (BIO). Available at: https://www.bio.org/sites/default/files/legacy/bioorg/docs/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf Accessed September 26, 2023
    PubMed
  • 8 Burstein D, Hunter DJ. “Why aren't we there yet?” Re-examining standard paradigms in imaging of OA: summary of the 2nd annual workshop on imaging based measures of osteoarthritis. Osteoarthritis Cartilage 2009; 17 (05) 571-578
    CrossrefPubMedGoogle Scholar
  • 9 Food and Drug Administration. Guidance for industry. clinical development programs for drugs, devices, and biological products intended for the treatment of osteoarthritis (OA). 1999. Available at: https://www.govinfo.gov/app/details/FR-1999-07-15/99-18031
    PubMed
  • 10 Altman R, Asch E, Bloch D. et al; Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Arthritis Rheum 1986; 29 (08) 1039-1049
    CrossrefPubMedGoogle Scholar
  • 11 Altman RD, Gold GE. Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthritis Cartilage 2007; 15 (Suppl A): A1-A56
    CrossrefPubMedGoogle Scholar
  • 12 Altman RD, Hochberg M, Murphy Jr WA, Wolfe F, Lequesne M. Atlas of individual radiographic features in osteoarthritis. Osteoarthritis Cartilage 1995; 3 (Suppl A): 3-70
    PubMedGoogle Scholar
  • 13 Scott Jr WW, Lethbridge-Cejku M, Reichle R, Wigley FM, Tobin JD, Hochberg MC. Reliability of grading scales for individual radiographic features of osteoarthritis of the knee. The Baltimore longitudinal study of aging atlas of knee osteoarthritis. Invest Radiol 1993; 28 (06) 497-501
    PubMedGoogle Scholar
  • 14 Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis 1957; 16 (04) 494-502
    CrossrefPubMedGoogle Scholar
  • 15 Leach RE, Gregg T, Siber FJ. Weight-bearing radiography in osteoarthritis of the knee. Radiology 1970; 97 (02) 265-268
    CrossrefPubMedGoogle Scholar
  • 16 Mazzuca SA, Brandt KD, Katz BP. Is conventional radiography suitable for evaluation of a disease-modifying drug in patients with knee osteoarthritis?. Osteoarthritis Cartilage 1997; 5 (04) 217-226
    CrossrefPubMedGoogle Scholar
  • 17 Buckland-Wright JC, Wolfe F, Ward RJ, Flowers N, Hayne C. Substantial superiority of semiflexed (MTP) views in knee osteoarthritis: a comparative radiographic study, without fluoroscopy, of standing extended, semiflexed (MTP), and schuss views. J Rheumatol 1999; 26 (12) 2664-2674
    PubMedGoogle Scholar
  • 18 Mazzuca SA, Brandt KD, Buckland-Wright JC. et al. Field test of the reproducibility of automated measurements of medial tibiofemoral joint space width derived from standardized knee radiographs. J Rheumatol 1999; 26 (06) 1359-1365
    PubMedGoogle Scholar
  • 19 Peterfy C, Li J, Zaim S. et al. Comparison of fixed-flexion positioning with fluoroscopic semi-flexed positioning for quantifying radiographic joint-space width in the knee: test-retest reproducibility. Skeletal Radiol 2003; 32 (03) 128-132
    CrossrefPubMedGoogle Scholar
  • 20 Ravaud P, Chastang C, Auleley GR. et al. Assessment of joint space width in patients with osteoarthritis of the knee: a comparison of 4 measuring instruments. J Rheumatol 1996; 23 (10) 1749-1755
    PubMedGoogle Scholar
  • 21 Duryea J, Zaim S, Genant HK. New radiographic-based surrogate outcome measures for osteoarthritis of the knee. Osteoarthritis Cartilage 2003; 11 (02) 102-110
    CrossrefPubMedGoogle Scholar
  • 22 Bruyère O, Henrotin YE, Honoré A. et al. Impact of the joint space width measurement method on the design of knee osteoarthritis studies. Aging Clin Exp Res 2003; 15 (02) 136-141
    CrossrefPubMedGoogle Scholar
  • 23 U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research. (CBER), Center for Devices and Radiological Health (CDRH). Osteoarthritis: Structural endpoints for the development of drugs, devices, and biological products for treatment guidance for industry. Available at: https://www.fda.gov/media/71132/download Accessed September 26, 2023
    PubMed
  • 24 Hunter DJ, Altman RD, Cicuttini F. et al. OARSI clinical trials recommendations: knee imaging in clinical trials in osteoarthritis. Osteoarthritis Cartilage 2015; 23 (05) 698-715
    CrossrefPubMedGoogle Scholar
  • 25 Crema MD, Nevitt MC, Guermazi A. et al. Progression of cartilage damage and meniscal pathology over 30 months is associated with an increase in radiographic tibiofemoral joint space narrowing in persons with knee OA—the MOST study. Osteoarthritis Cartilage 2014; 22 (10) 1743-1747
    CrossrefPubMedGoogle Scholar
  • 26 Guermazi A, Niu J, Hayashi D. et al. Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study). BMJ 2012; 345: e5339
    CrossrefPubMedGoogle Scholar
  • 27 Eckstein F, Burstein D, Link TM. Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis. NMR Biomed 2006; 19 (07) 822-854
    CrossrefPubMedGoogle Scholar
  • 28 Eckstein F, Ateshian G, Burgkart R. et al. Proposal for a nomenclature for magnetic resonance imaging based measures of articular cartilage in osteoarthritis. Osteoarthritis Cartilage 2006; 14 (10) 974-983
    CrossrefPubMedGoogle Scholar
  • 29 Crema MD, Roemer FW, Marra MD. et al. Articular cartilage in the knee: current MR imaging techniques and applications in clinical practice and research. Radiographics 2011; 31 (01) 37-61
    CrossrefPubMedGoogle Scholar
  • 30 Eckstein F, Wirth W, Nevitt MC. Recent advances in osteoarthritis imaging—the Osteoarthritis Initiative. Nat Rev Rheumatol 2012; 8 (10) 622-630
    CrossrefPubMedGoogle Scholar
  • 31 Buck RJ, Wirth W, Dreher D, Nevitt M, Eckstein F. Frequency and spatial distribution of cartilage thickness change in knee osteoarthritis and its relation to clinical and radiographic covariates—data from the osteoarthritis initiative. Osteoarthritis Cartilage 2013; 21 (01) 102-109
    CrossrefPubMedGoogle Scholar
  • 32 Eckstein F, Hochberg MC, Guehring H. et al. Long-term structural and symptomatic effects of intra-articular sprifermin in patients with knee osteoarthritis: 5-year results from the FORWARD study. Ann Rheum Dis 2021; 80 (08) 1062-1069
    CrossrefPubMedGoogle Scholar
  • 33 Guehring H, Moreau F, Daelken B. et al. The effects of sprifermin on symptoms and structure in a subgroup at risk of progression in the FORWARD knee osteoarthritis trial. Semin Arthritis Rheum 2021; 51 (02) 450-456
    CrossrefPubMedGoogle Scholar
  • 34 Wirth W, Nevitt M, Hellio Le Graverand MP. et al; OA Initiative Investigators Group. Lateral and medial joint space narrowing predict subsequent cartilage loss in the narrowed, but not in the non-narrowed femorotibial compartment—data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 2014; 22 (01) 63-70
    CrossrefPubMedGoogle Scholar
  • 35 Imbert O, Deckx H, Bernard K. et al. The design of a randomized, placebo-controlled, dose-ranging trial to investigate the efficacy and safety of the ADAMTS-5 inhibitor S201086/GLPG1972 in knee osteoarthritis. Osteoarthr Cartil Open 2021; 3 (04) 100209
    CrossrefPubMedGoogle Scholar
  • 36 Eckstein F, Maschek S, Culvenor A. et al. Which risk factors determine cartilage thickness and composition change in radiographically normal knees? Data from the Osteoarthritis Initiative. Osteoarthr Cartil Open 2023; 5 (03) 100365
    CrossrefPubMedGoogle Scholar
  • 37 Buck RJ, Wyman BT, Le Graverand MP, Hudelmaier M, Wirth W, Eckstein F. 9001140 A investigators. Osteoarthritis may not be a one-way-road of cartilage loss—comparison of spatial patterns of cartilage change between osteoarthritic and healthy knees. Osteoarthritis Cartilage 2010; 18 (03) 329-335
    CrossrefPubMedGoogle Scholar
  • 38 Eckstein F, Buck R, Wirth W. Location-independent analysis of structural progression of osteoarthritis—taking it all apart, and putting the puzzle back together makes the difference. Semin Arthritis Rheum 2017; 46 (04) 404-410
    CrossrefPubMedGoogle Scholar
  • 39 Buck RJ, Wyman BT, Le Graverand MP, Hudelmaier M, Wirth W, Eckstein F. A9001140 Investigators. Does the use of ordered values of subregional change in cartilage thickness improve the detection of disease progression in longitudinal studies of osteoarthritis?. Arthritis Rheum 2009; 61 (07) 917-924
    CrossrefPubMedGoogle Scholar
  • 40 Eckstein F, Le Graverand MP, Charles HC. et al; A9001140, investigators. Clinical, radiographic, molecular and MRI-based predictors of cartilage loss in knee osteoarthritis. Ann Rheum Dis 2011; 70 (07) 1223-1230
    CrossrefPubMedGoogle Scholar
  • 41 Eckstein F, Kraines JL, Aydemir A, Wirth W, Maschek S, Hochberg MC. Intra-articular sprifermin reduces cartilage loss in addition to increasing cartilage gain independent of location in the femorotibial joint: post-hoc analysis of a randomised, placebo-controlled phase II clinical trial. Ann Rheum Dis 2020; 79 (04) 525-528
    CrossrefPubMedGoogle Scholar
  • 42 Eckstein F, Maschek S, Roemer FW, Duda GN, Sharma L, Wirth W. Cartilage loss in radiographically normal knees depends on radiographic status of the contralateral knee—data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 2019; 27 (02) 273-277
    CrossrefPubMedGoogle Scholar
  • 43 Eckstein F, Kwoh CK, Boudreau RM. et al; OAI investigators. Quantitative MRI measures of cartilage predict knee replacement: a case-control study from the Osteoarthritis Initiative. Ann Rheum Dis 2013; 72 (05) 707-714
    CrossrefPubMedGoogle Scholar
  • 44 Bowes MA, Kacena K, Alabas OA. et al. Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative. Ann Rheum Dis 2021; 80 (04) 502-508
    CrossrefPubMedGoogle Scholar
  • 45 Bloecker K, Wirth W, Guermazi A. et al. Relationship between medial meniscal extrusion and cartilage loss in specific femorotibial subregions: data from the Osteoarthritis Initiative. Arthritis Care Res (Hoboken) 2015; 67 (11) 1545-1552
    CrossrefPubMedGoogle Scholar
  • 46 Sharma K, Eckstein F, Maschek S, Roth M, Hunter DJ, Wirth W. Association of quantitative measures of medial meniscal extrusion with structural and symptomatic knee osteoarthritis progression—data from the OAI FNIH biomarker study. Osteoarthritis Cartilage 2023; 31 (10) 1396-1404
    CrossrefPubMedGoogle Scholar
  • 47 Cai J, Xu J, Wang K. et al. Association between infrapatellar fat pad volume and knee structural changes in patients with knee osteoarthritis. J Rheumatol 2015; 42 (10) 1878-1884
    CrossrefPubMedGoogle Scholar
  • 48 Peterfy CG, Guermazi A, Zaim S. et al. Whole-Organ Magnetic Resonance Imaging Score (WORMS) of the knee in osteoarthritis. Osteoarthritis Cartilage 2004; 12 (03) 177-190
    CrossrefPubMedGoogle Scholar
  • 49 Kornaat PR, Ceulemans RY, Kroon HM. et al. MRI assessment of knee osteoarthritis: Knee Osteoarthritis Scoring System (KOSS)—inter-observer and intra-observer reproducibility of a compartment-based scoring system. Skeletal Radiol 2005; 34 (02) 95-102
    CrossrefPubMedGoogle Scholar
  • 50 Hunter DJ, Lo GH, Gale D, Grainger AJ, Guermazi A, Conaghan PG. The reliability of a new scoring system for knee osteoarthritis MRI and the validity of bone marrow lesion assessment: BLOKS (Boston Leeds Osteoarthritis Knee Score). Ann Rheum Dis 2008; 67 (02) 206-211
    CrossrefPubMedGoogle Scholar
  • 51 Hunter DJ, Guermazi A, Lo GH. et al. Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score). Osteoarthritis Cartilage 2011; 19 (08) 990-1002
    CrossrefPubMedGoogle Scholar
  • 52 Guermazi A, Roemer FW, Hayashi D. et al. Assessment of synovitis with contrast-enhanced MRI using a whole-joint semiquantitative scoring system in people with, or at high risk of, knee osteoarthritis: the MOST study. Ann Rheum Dis 2011; 70 (05) 805-811
    CrossrefPubMedGoogle Scholar
  • 53 Baker K, Grainger A, Niu J. et al. Relation of synovitis to knee pain using contrast-enhanced MRIs. Ann Rheum Dis 2010; 69 (10) 1779-1783
    CrossrefPubMedGoogle Scholar
  • 54 Rhodes LA, Grainger AJ, Keenan AM, Thomas C, Emery P, Conaghan PG. The validation of simple scoring methods for evaluating compartment-specific synovitis detected by MRI in knee osteoarthritis. Rheumatology (Oxford) 2005; 44 (12) 1569-1573
    CrossrefPubMedGoogle Scholar
  • 55 Roemer FW, Frobell R, Lohmander LS, Niu J, Guermazi A. Anterior Cruciate Ligament OsteoArthritis Score (ACLOAS): longitudinal MRI-based whole joint assessment of anterior cruciate ligament injury. Osteoarthritis Cartilage 2014; 22 (05) 668-682
    CrossrefPubMedGoogle Scholar
  • 56 Marlovits S, Singer P, Zeller P, Mandl I, Haller J, Trattnig S. Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) for the evaluation of autologous chondrocyte transplantation: determination of interobserver variability and correlation to clinical outcome after 2 years. Eur J Radiol 2006; 57 (01) 16-23
    CrossrefPubMedGoogle Scholar
  • 57 Schreiner MM, Raudner M, Marlovits S. et al. The MOCART (Magnetic Resonance Observation of Cartilage Repair Tissue) 2.0 Knee Score and Atlas. Cartilage 2021; 13 (1_Suppl): 571S-587S
    CrossrefPubMedGoogle Scholar
  • 58 Roemer FW, Guermazi A, Trattnig S. et al. Whole joint MRI assessment of surgical cartilage repair of the knee: cartilage repair osteoarthritis knee score (CROAKS). Osteoarthritis Cartilage 2014; 22 (06) 779-799
    CrossrefPubMedGoogle Scholar
  • 59 Hunter DJ, Zhang W, Conaghan PG. et al. Responsiveness and reliability of MRI in knee osteoarthritis: a meta-analysis of published evidence. Osteoarthritis Cartilage 2011; 19 (05) 589-605
    CrossrefPubMedGoogle Scholar
  • 60 Maksymowych WP, Jaremko JL, Pedersen SJ. et al. Comparative validation of the knee inflammation MRI scoring system and the MRI osteoarthritis knee score for semi-quantitative assessment of bone marrow lesions and synovitis-effusion in osteoarthritis: an international multi-reader exercise. Ther Adv Musculoskelet Dis 2023; 15: X231171766
    CrossrefPubMedGoogle Scholar
  • 61 Crema MD, Roemer FW, Nevitt MC. et al. Cross-sectional and longitudinal reliability of semiquantitative osteoarthritis assessment at 1.0T extremity MRI: multi-reader data from the MOST study. Osteoarthr Cartil Open 2021; 3 (04) 100214
    CrossrefPubMedGoogle Scholar
  • 62 Mobasheri A, Saarakkala S, Finnilä M, Karsdal MA, Bay-Jensen AC, van Spil WE. Recent advances in understanding the phenotypes of osteoarthritis. F1000 Res 2019; 8: 8
    CrossrefPubMedGoogle Scholar
  • 63 Bijlsma JW, Berenbaum F, Lafeber FP. Osteoarthritis: an update with relevance for clinical practice. Lancet 2011; 377 (9783): 2115-2126
    CrossrefPubMedGoogle Scholar
  • 64 Deveza LA, Melo L, Yamato TP, Mills K, Ravi V, Hunter DJ. Knee osteoarthritis phenotypes and their relevance for outcomes: a systematic review. Osteoarthritis Cartilage 2017; 25 (12) 1926-1941
    CrossrefPubMedGoogle Scholar
  • 65 Roemer FW, Collins JE, Neogi T, Crema MD, Guermazi A. Association of knee OA structural phenotypes to risk for progression: a secondary analysis from the Foundation for National Institutes of Health Osteoarthritis Biomarkers study (FNIH). Osteoarthritis Cartilage 2020; 28 (09) 1220-1228
    CrossrefPubMedGoogle Scholar
  • 66 Roemer FW, Collins J, Kwoh CK. et al. MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES). Osteoarthritis Cartilage 2020; 28 (01) 71-81
    CrossrefPubMedGoogle Scholar
  • 67 Guermazi A, Roemer FW, Jarraya M, Hayashi D. A call for screening MRI as a tool for osteoarthritis clinical trials. Skeletal Radiol 2023; 52 (11) 2011-2019
    CrossrefPubMedGoogle Scholar
  • 68 Roemer FW, Nevitt MC, Felson DT. et al. Predictive validity of within-grade scoring of longitudinal changes of MRI-based cartilage morphology and bone marrow lesion assessment in the tibio-femoral joint—the MOST study. Osteoarthritis Cartilage 2012; 20 (11) 1391-1398
    CrossrefPubMedGoogle Scholar
  • 69 Felson DT, Nevitt MC, Yang M. et al. A new approach yields high rates of radiographic progression in knee osteoarthritis. J Rheumatol 2008; 35 (10) 2047-2054
    PubMedGoogle Scholar
  • 70 Roemer F, Maschek S, Wisser A. et al. Worsening of articular tissue damage as defined by semi-quantitative MRI is associated with concurrent quantitative cartilage loss over 24 months. Cartilage 2023; 14 (01) 39-47
    CrossrefPubMedGoogle Scholar
  • 71 Roemer FW, Guermazi A, Javaid MK. et al; MOST Study investigators. Change in MRI-detected subchondral bone marrow lesions is associated with cartilage loss: the MOST Study. A longitudinal multicentre study of knee osteoarthritis. Ann Rheum Dis 2009; 68 (09) 1461-1465
    CrossrefPubMedGoogle Scholar
  • 72 Ross PD, Huang C, Karpf D. et al. Blinded reading of radiographs increases the frequency of errors in vertebral fracture detection. J Bone Miner Res 1996; 11 (11) 1793-1800
    CrossrefPubMedGoogle Scholar
  • 73 Bruynesteyn K, Van Der Heijde D, Boers M. et al. Detecting radiological changes in rheumatoid arthritis that are considered important by clinical experts: influence of reading with or without known sequence. J Rheumatol 2002; 29 (11) 2306-2312
    PubMedGoogle Scholar
  • 74 Felson DT, Nevitt MC. Blinding images to sequence in osteoarthritis: evidence from other diseases. Osteoarthritis Cartilage 2009; 17 (03) 281-283
    CrossrefPubMedGoogle Scholar
  • 75 Chan WP, Lang P, Stevens MP. et al. Osteoarthritis of the knee: comparison of radiography, CT, and MR imaging to assess extent and severity. AJR Am J Roentgenol 1991; 157 (04) 799-806
    CrossrefPubMedGoogle Scholar
  • 76 Roemer FW, Engelke K, Li L, Laredo JD, Guermazi A. MRI underestimates presence and size of knee osteophytes using CT as a reference standard. Osteoarthritis Cartilage 2023; 31 (05) 656-668
    CrossrefPubMedGoogle Scholar
  • 77 Haubner M, Eckstein F, Schnier M. et al. A non-invasive technique for 3-dimensional assessment of articular cartilage thickness based on MRI. Part 2: Validation using CT arthrography. Magn Reson Imaging 1997; 15 (07) 805-813
    CrossrefPubMedGoogle Scholar
  • 78 Vande Berg BC, Lecouvet FE, Poilvache P. et al. Assessment of knee cartilage in cadavers with dual-detector spiral CT arthrography and MR imaging. Radiology 2002; 222 (02) 430-436
    CrossrefPubMedGoogle Scholar
  • 79 Vande Berg BC, Lecouvet FE, Poilvache P, Dubuc JE, Maldague B, Malghem J. Anterior cruciate ligament tears and associated meniscal lesions: assessment at dual-detector spiral CT arthrography. Radiology 2002; 223 (02) 403-409
    CrossrefPubMedGoogle Scholar
  • 80 Omoumi P, Babel H, Jolles BM, Favre J. Relationships between cartilage thickness and subchondral bone mineral density in non-osteoarthritic and severely osteoarthritic knees: In vivo concomitant 3D analysis using CT arthrography. Osteoarthritis Cartilage 2019; 27 (04) 621-629
    CrossrefPubMedGoogle Scholar
  • 81 Kokkonen HT, Suomalainen JS, Joukainen A. et al. In vivo diagnostics of human knee cartilage lesions using delayed CBCT arthrography. J Orthop Res 2014; 32 (03) 403-412
    CrossrefPubMedGoogle Scholar
  • 82 Johnson TR, Krauss B, Sedlmair M. et al. Material differentiation by dual energy CT: initial experience. Eur Radiol 2007; 17 (06) 1510-1517
    CrossrefPubMedGoogle Scholar
  • 83 Ea HK, Nguyen C, Bazin D. et al. Articular cartilage calcification in osteoarthritis: insights into crystal-induced stress. Arthritis Rheum 2011; 63 (01) 10-18
    CrossrefPubMedGoogle Scholar
  • 84 Li M, Qu Y, Song B. Meta-analysis of dual-energy computed tomography virtual non-calcium imaging to detect bone marrow edema. Eur J Radiol 2017; 95: 124-129
    CrossrefPubMedGoogle Scholar
  • 85 Segal NA, Li S. WBCT and its evolving role in OA research and clinical practice. Osteoarthr Imaging 2022; 2: 100083
    CrossrefPubMedGoogle Scholar
  • 86 Segal NA, Nevitt MC, Lynch JA, Niu J, Torner JC, Guermazi A. Diagnostic performance of 3D standing CT imaging for detection of knee osteoarthritis features. Phys Sportsmed 2015; 43 (03) 213-220
    CrossrefPubMedGoogle Scholar
  • 87 Segal NA, Bergin J, Kern A, Findlay C, Anderson DD. Test-retest reliability of tibiofemoral joint space width measurements made using a low-dose standing CT scanner. Skeletal Radiol 2017; 46 (02) 217-222
    CrossrefPubMedGoogle Scholar
  • 88 Segal NA, Frick E, Duryea J. et al. Comparison of tibiofemoral joint space width measurements from standing CT and fixed flexion radiography. J Orthop Res 2017; 35 (07) 1388-1395
    CrossrefPubMedGoogle Scholar
  • 89 Kothari MD, Rabe KG, Anderson DD. et al; Multicenter Osteoarthritis Study Group. The relationship of three-dimensional joint space width on weight bearing CT with pain and physical function. J Orthop Res 2019 December 16 (Epub ahead of print)
    PubMedGoogle Scholar
  • 90 Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 2018; 8 (01) 1727
    CrossrefPubMedGoogle Scholar
  • 91 Norman B, Pedoia V, Majumdar S. Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology 2018; 288 (01) 177-185
    CrossrefPubMedGoogle Scholar
  • 92 Liu F, Zhou Z, Samsonov A. et al. Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 2018; 289 (01) 160-169
    CrossrefPubMedGoogle Scholar
  • 93 Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging 2019; 49 (02) 400-410
    CrossrefPubMedGoogle Scholar
  • 94 Chang PD, Wong TT, Rasiej MJ. Deep learning for detection of complete anterior cruciate ligament tear. J Digit Imaging 2019; 32 (06) 980-986
    CrossrefPubMedGoogle Scholar
  • 95 Brabec JKT, Franc V, Machlica L. On model evaluation under non-constant class imbalance. Comput Sci 2020; 12140: 74-87
    PubMedGoogle Scholar
  • 96 Liu S, Roemer F, Ge Y. et al. Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies. Osteoarthritis Cartilage 2023; 31 (09) 1242-1248
    CrossrefPubMedGoogle Scholar
  • 97 Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv 2013; 16 (Pt 2): 246-253
    PubMedGoogle Scholar
  • 98 Ambellan F, Tack A, Ehlke M, Zachow S. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the Osteoarthritis Initiative. Med Image Anal 2019; 52: 109-118
    CrossrefPubMedGoogle Scholar
  • 99 Wirth W, Eckstein F, Kemnitz J. et al. Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. Magn Reson Mater Biol Phys Med 2021; 34 (03) 337-354
    CrossrefPubMedGoogle Scholar
  • 100 Eckstein F, Chaudhari AS, Fuerst D. et al. Detection of differences in longitudinal cartilage thickness loss using a deep-learning automated segmentation algorithm: data from the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative. Arthritis Care Res (Hoboken) 2022; 74 (06) 929-936
    CrossrefPubMedGoogle Scholar
  • 101 Eckstein F, Collins JE, Nevitt MC. et al; FNIH OA Biomarkers Consortium. 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 Rheumatol 2015; 67 (12) 3184-3189
    CrossrefPubMedGoogle Scholar
  • 102 Roemer FW, Guermazi A, Collins JE. et al. Semi-quantitative MRI biomarkers of knee osteoarthritis progression in the FNIH biomarkers consortium cohort.  Methodologic aspects and definition of change. BMC Musculoskelet Disord 2016; 17 (01) 466
    CrossrefPubMedGoogle Scholar
  • 103 Collins JE, Losina E, Nevitt MC. et al. Semiquantitative imaging biomarkers of knee osteoarthritis progression: data from the Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheumatol 2016; 68 (10) 2422-2431
    CrossrefPubMedGoogle Scholar
  • 104 Kraus VB, Collins JE, Charles HC. et al; OA Biomarkers Consortium. Predictive validity of radiographic trabecular bone texture in knee osteoarthritis: the Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Rheumatol 2018; 70 (01) 80-87
    CrossrefPubMedGoogle Scholar
  • 105 Kraus VB, Collins JE, Hargrove D. et al; OA Biomarkers Consortium. Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium. Ann Rheum Dis 2017; 76 (01) 186-195
    CrossrefPubMedGoogle Scholar
  • 106 Hunter DJ, Deveza LA, Collins JE. et al. Multivariable modeling of biomarker data from the Phase I Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium. Arthritis Care Res (Hoboken) 2022; 74 (07) 1142-1153
    CrossrefPubMedGoogle Scholar
  • 107 Hunter DJ, Collins JE, Deveza L, Hoffmann SC, Kraus VB. Biomarkers in osteoarthritis: current status and outlook: the FNIH Biomarkers Consortium PROGRESS OA study. Skeletal Radiol 2023; 52 (11) 2323-2339
    CrossrefPubMedGoogle Scholar