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Beynon RA, Saunders FR, Ebsim R, Frysz M, Faber BG, Gregory JS, Lindner C, Sarmanova A, Aspden RM, Harvey NC, Cootes T, Tobias JH. Dual-energy X-ray absorptiometry derived knee shape may provide a useful imaging biomarker for predicting total knee replacement: Findings from a study of 37,843 people in UK Biobank. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100468. [PMID: 38655015 PMCID: PMC11035060 DOI: 10.1016/j.ocarto.2024.100468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/26/2024] Open
Abstract
Objective We aimed to create an imaging biomarker for knee shape using knee dual-energy x-ray absorptiometry (DXA) scans and investigate its potential association with subsequent total knee replacement (TKR), independently of radiographic features of knee osteoarthritis and established risk factors. Methods Using a 129-point statistical shape model, knee shape (expressed as a B-score) and minimum joint space width (mJSW) of the medial joint compartment (binarized as above or below the first quartile) were derived. Osteophytes were manually graded in a subset of images and an overall score was assigned. Cox proportional hazards models were used to examine the associations of B-score, mJSW and osteophyte score with TKR risk, adjusting for age, sex, height and weight. Results The analysis included 37,843 individuals (mean age 63.7 years). In adjusted models, B-score was associated with TKR: each unit increase in B-score, reflecting one standard deviation from the mean healthy shape, corresponded to a hazard ratio (HR) of 2.25 (2.08, 2.43), while a lower mJSW had a HR of 2.28 (1.88, 2.77). Among the 6719 images scored for osteophytes, mJSW was replaced by osteophyte score in the most strongly predictive model for TKR. In ROC analyses, a model combining B-score, osteophyte score, and demographics outperformed a model including demographics alone (AUC = 0.87 vs 0.73). Conclusions Using statistical shape modelling, we derived a DXA-based imaging biomarker for knee shape that was associated with kOA progression. When combined with osteophytes and demographic data, this biomarker may help identify individuals at high risk of TKR, facilitating targeted interventions.
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Affiliation(s)
- Rhona A. Beynon
- University of Bristol, Musculoskeletal Research Unit, Bristol Medical School, Bristol, United Kingdom
| | - Fiona R. Saunders
- University of Aberdeen, Centre for Arthritis and Musculoskeletal Health, Aberdeen, United Kingdom
| | - Raja Ebsim
- The University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom
| | - Monika Frysz
- University of Bristol, Musculoskeletal Research Unit, Bristol Medical School, Bristol, United Kingdom
- University of Bristol, Medical Research Council Integrative Epidemiology Unit, Bristol, United Kingdom
| | - Benjamin G. Faber
- University of Bristol, Musculoskeletal Research Unit, Bristol Medical School, Bristol, United Kingdom
- University of Bristol, Medical Research Council Integrative Epidemiology Unit, Bristol, United Kingdom
| | - Jennifer S. Gregory
- University of Aberdeen, Centre for Arthritis and Musculoskeletal Health, Aberdeen, United Kingdom
| | - Claudia Lindner
- The University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom
| | - Aliya Sarmanova
- University of Bristol, Musculoskeletal Research Unit, Bristol Medical School, Bristol, United Kingdom
| | - Richard M. Aspden
- University of Aberdeen, Centre for Arthritis and Musculoskeletal Health, Aberdeen, United Kingdom
| | - Nicholas C. Harvey
- University of Southampton, MRC Lifecourse Epidemiology Centre, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, United Kingdom
| | - Timothy Cootes
- The University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, United Kingdom
| | - Jonathan H. Tobias
- University of Bristol, Musculoskeletal Research Unit, Bristol Medical School, Bristol, United Kingdom
- University of Bristol, Medical Research Council Integrative Epidemiology Unit, Bristol, United Kingdom
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Buldt AK, Gregory JS, Munteanu SE, Allan JJ, Tan JM, Auhl M, Landorf KB, Roddy E, Marshall M, Menz HB. Association of Bone Shape and Alignment Analyzed Using Statistical Shape Modeling With Severity of First Metatarsophalangeal Joint Osteoarthritis. Arthritis Care Res (Hoboken) 2024; 76:385-392. [PMID: 37728065 DOI: 10.1002/acr.25237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/22/2023] [Accepted: 09/14/2023] [Indexed: 09/21/2023]
Abstract
OBJECTIVE We aimed to explore the relationship between bone shape and radiographic severity in individuals with first metatarsophalangeal joint osteoarthritis (first MTP joint OA). METHODS Weightbearing lateral and dorsoplantar radiographs were obtained for the symptomatic foot of 185 participants (105 females, aged 22 to 85 years) with clinically diagnosed first MTP joint OA. Participants were classified into none/mild, moderate, or severe categories using a standardized atlas. An 80-point model for lateral radiographs and 77-point model for dorsoplantar radiographs was used to define independent modes of variation using statistical shape modeling software. Odds ratios adjusted for confounders were calculated using ordinal regression to determine the association between radiographic severity and mode scores. RESULTS After assessment and grading of radiographs, 35 participants (18.9%) were included in the none/mild first MTP joint OA severity category, 69 (37.2%) in the moderate severity category, and 81 (43.7%) in the severe category. For lateral-view radiographs, 16 modes of variation were included, which collectively represented 83.2% of total shape variance. Of these, four modes were associated with radiographic severity. For dorsoplantar-view radiographs, 15 modes of variation were included, representing 82.6% of total shape variance. Of these, six modes were associated with radiographic severity. CONCLUSIONS Variations in the shape and alignment of the medial cuneiform, first metatarsal, and proximal and distal phalanx of the hallux are significantly associated with radiographic severity of first MTP joint OA. Prospective studies are required to determine whether bone shape characteristics are associated with the development and/or progression of this condition.
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Affiliation(s)
| | | | | | | | - Jade M Tan
- The University of Western Australia, Crawley, Perth, Western Australia, Australia
| | - Maria Auhl
- La Trobe University, Melbourne, Victoria, Australia
| | | | - Edward Roddy
- Keele University, Keele, Staffordshire, UK and Midlands Partnership University NHS Foundation Trust, Haywood Hospital, Burslem, Staffordshire, UK
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Gao KT, Xie E, Chen V, Iriondo C, Calivà F, Souza RB, Majumdar S, Pedoia V. Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis. Arthritis Rheumatol 2023; 75:1958-1968. [PMID: 37262347 DOI: 10.1002/art.42623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/24/2023] [Accepted: 05/25/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness. METHODS A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration. RESULTS Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears. CONCLUSION This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.
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Affiliation(s)
- Kenneth T Gao
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Emily Xie
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Vincent Chen
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Claudia Iriondo
- University of California, San Francisco and University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, San Francisco, United States
| | - Francesco Calivà
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Richard B Souza
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco and Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, United States
| | - Sharmila Majumdar
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
| | - Valentina Pedoia
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
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Aspden RM. Subchondral bone - a welcome distraction in OA treatment. Osteoarthritis Cartilage 2022; 30:911-912. [PMID: 35247544 DOI: 10.1016/j.joca.2022.02.617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/10/2022] [Accepted: 02/23/2022] [Indexed: 02/02/2023]
Affiliation(s)
- R M Aspden
- Aberdeen Centre for Arthritis and Musculoskeletal Health, Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition, Foresterhill, Aberdeen AB25 2ZD, UK.
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Oei EHG, Hirvasniemi J, van Zadelhoff TA, van der Heijden RA. Osteoarthritis year in review 2021: imaging. Osteoarthritis Cartilage 2022; 30:226-236. [PMID: 34838670 DOI: 10.1016/j.joca.2021.11.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/16/2021] [Accepted: 11/11/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging. METHODS The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further. RESULTS The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging. CONCLUSION Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.
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Affiliation(s)
- E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - T A van Zadelhoff
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
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Abstract
PURPOSE OF REVIEW To review the recent literature on bone in osteoarthritis (OA), with a focus on imaging and intervention studies. RECENT FINDINGS Most studies focused on knee OA; hip and hand studies were uncommon. Bone shape studies demonstrated that shape changes precede radiographic OA, predict joint replacement, and have demonstrated high responsiveness. Novel quantitative 3D imaging markers (B-score) have better characterized OA severity, including preradiographic OA status. The addition of computerized tomography-derived 3D metrics has improved the prediction of hip joint replacement when compared to radiographs alone.Recent studies of bisphosphonates for knee OA have reported no benefits on pain or bone marrow lesion (BML) size. A meta-analysis on Vitamin D supplementation in knee OA suggested minimal symptom improvement and no benefits on the structure. Cathepsin K inhibition demonstrated reduction in OA bone change progression, but with no symptom benefit. Studies of injections of bone substitutes into BMLs (subchondroplasty) have generally been small and potential benefits remain unclear. SUMMARY Subchondral bone features are associated with pain, incidence and progression of OA. Recent studies have validated quantitative bone shape as a biomarker for OA trials. Trials of bone-targeted OA therapies have been disappointing although cathepsin K inhibition may slow structural progression.
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Affiliation(s)
- Kiran Khokhar
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds and NIHR Leeds Biomedical Research Centre, Leeds, UK
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Gao KT, Pedoia V, Young KA, Kogan F, Koff MF, Gold GE, Potter HG, Majumdar S. Multiparametric MRI characterization of knee articular cartilage and subchondral bone shape in collegiate basketball players. J Orthop Res 2021; 39:1512-1522. [PMID: 32910520 PMCID: PMC8359246 DOI: 10.1002/jor.24851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/31/2020] [Accepted: 09/02/2020] [Indexed: 02/04/2023]
Abstract
Magnetic resonance imaging (MRI) is commonly used to evaluate the morphology of the knee in athletes with high-knee impact; however, complex repeated loading of the joint can lead to biochemical and structural degeneration that occurs before visible morphological changes. In this study, we utilized multiparametric quantitative MRI to compare morphology and composition of articular cartilage and subchondral bone shape between young athletes with high-knee impact (basketball players; n = 40) and non-knee impact (swimmers; n = 25). We implemented voxel-based relaxometry to register all cases to a single reference space and performed a localized compositional analysis of T 1ρ - and T 2 -relaxation times on a voxel-by-voxel basis. Additionally, statistical shape modeling was employed to extract differences in subchondral bone shape between the two groups. Evaluation of cartilage composition demonstrated a significant prolongation of relaxation times in the medial femoral and tibial compartments and in the posterolateral femur of basketball players in comparison to relaxation times in the same cartilage compartments of swimmers. The compositional analysis also showed depth-dependent differences with prolongation of the superficial layer in basketball players. For subchondral bone shape, three total modes were found to be significantly different between groups and related to the relative sizes of the tibial plateaus, intercondylar eminences, and the curvature and concavity of the patellar lateral facet. In summary, this study identified several characteristics associated with a high-knee impact which may expand our understanding of local degenerative patterns in this population.
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Affiliation(s)
- Kenneth T. Gao
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Valentina Pedoia
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Feliks Kogan
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Matthew F. Koff
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Garry E. Gold
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Hollis G. Potter
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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