Patterns of progression differ between Kellgren-Lawrence 2 and 3 knees fulfilling different definitions of a cartilage-meniscus phenotype in the Foundation for National Institutes of Health Osteoarthritis Biomarkers study (FNIH)

Phenotypic variations in knee osteoarthritis: insights from MRI and radiographic comparisons

OBJECTIVE

To investigate the correlation between MRI-based phenotypes (cartilage-meniscus, subchondral bone, and inflammatory) and radiography-based atrophic and hypertrophic phenotypes, aiming to demonstrate MRI's diagnostic capability in identifying complex osteoarthritis phenotypes that radiography cannot fully capture.

MATERIALS AND METHODS

This single-center retrospective study examined knee radiographs and MRIs of patients from November 2021 to April 2023 to identify osteoarthritis phenotypes. Radiographs were staged by the Kellgren-Lawrence system, and both modalities were classified into atrophic or hypertrophic phenotypes. MRIs were further classified into three phenotypes: cartilage-meniscus, subchondral bone, and inflammatory. Associations between phenotypes, Kellgren-Lawrence stage, age, and gender were analyzed with Pearson chi-square test and student T-test. Reliability measurements were evaluated using kappa statistic.

RESULTS

A total of 214 knees from 187 individuals (73.3% women, 26.7% men; mean age 57.1 ± 9.1 years) were included. The hypertrophic MRI phenotype was significantly associated with cartilage-meniscus and subchondral bone phenotypes (p < 0.001). Cartilage-meniscus and subchondral bone phenotypes were less prevalent in Kellgren-Lawrence stage 2 than in stages 3 and 4 (p < 0.001 and p = 0.004, respectively). The subchondral bone phenotype was more common in men (p = 0.022), and the cartilage-meniscus phenotype in the elderly (p < 0.001). Radiography and MRI had substantial agreement (Kappa = 0.637, p < 0.001) in diagnosing hypertrophic and atrophic phenotypes.

CONCLUSION

The hypertrophic phenotype was associated with cartilage-meniscus and subchondral bone phenotypes, with lower prevalences in Kellgren-Lawrence stage 2 knees. MRI offers enhanced phenotypic characterization, which facilitates more precise and individualized management in osteoarthritis care. Despite limitations compared to MRI, radiography remains valuable for the evaluation of hypertrophic and atrophic phenotypes.

Association of MRI-based knee osteoarthritis structural phenotypes with short-term structural progression and subsequent total knee replacement

BACKGROUND

The failure of disease-modifying osteoarthritis drugs (DMOADs) trials lies mainly in the heterogeneity of the disease, which calls for a more precise population with specific progression and outcomes. This study aimed to determine whether and which MRI-based structural phenotype of knee osteoarthritis (KOA) is associated with short-term structural progression and subsequent total knee replacement (TKR).

METHODS

A longitudinal study was conducted among participants with baseline Kellgren-Lawrence grade (KLG) ≥ 2 from the Osteoarthritis Initiative (OAI). The structural phenotypes at baseline were defined as subchondral bone, meniscus/cartilage and inflammatory phenotypes according to the MRI Osteoarthritis Knee Score (MOAKS). The primary outcome was the progression of structural abnormalities within 24 months and multivariable logistic regressions were applied to evaluate the associations. The secondary outcome was the incidence of TKR during 108 months. Cox regressions and Kaplan-Meier survival curves were used for the analysis.

RESULTS

A total of 733 participants with KOA were finally included in our study, with 493 (67.3%) having the three main structural phenotypes. For the primary outcome, the subchondral bone phenotype (OR [95% CI]:1.71 [1.02, 2.83], 1.52 [1.06, 2.18], 1.65 [1.11, 2.42], respectively) and the inflammatory phenotype (OR [95% CI]: 1.69 [1.05, 2.74], 1.82 [1.31, 2.52], 2.15 [1.48, 3.14], respectively) were both associated with the short-term progression of joint space narrowing, osteophytes and sclerosis in 24 months, whereas the meniscus/cartilage phenotype was only associated with the progression of osteophytes and sclerosis. For the secondary outcome, the subchondral bone phenotype (HR [95% CI]: 1.71 [1.06-2.78]) and inflammatory phenotype (HR [95%CI]: 2.00 [1.02-2.67]) were associated with shorter time to subsequent TKR, but not the meniscus/cartilage phenotype. Besides, the cumulative effect when the structural phenotype overlapped was confirmed in both outcomes.

CONCLUSIONS

The subchondral bone phenotype and inflammatory phenotype were associated with the progression of joint space narrowing, osteophytes and sclerosis in 24 months, along with subsequent TKR in 108 months. Besides, additive effects of overlapped phenotypes were further determined. These phenotypes could serve as valuable screening tools for future clinical trials and provide guidance for risk evaluation.

Osteoarthritis year in review 2023: Imaging

PURPOSE

This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023.

METHODS

A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review.

RESULTS

Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest.

CONCLUSIONS

Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.

Structural phenotypes of knee osteoarthritis: potential clinical and research relevance

A joint contains many different tissues that can exhibit pathological changes, providing many potential targets for treatment. Researchers are increasingly suggesting that osteoarthritis (OA) comprises several phenotypes or subpopulations. Consequently, a treatment for OA that targets only one pathophysiologic abnormality is unlikely to be similarly efficacious in preventing or delaying the progression of all the different phenotypes of structural OA. Five structural phenotypes have been proposed, namely the inflammatory, meniscus-cartilage, subchondral bone, and atrophic and hypertrophic phenotypes. The inflammatory phenotype is characterized by marked synovitis and/or joint effusion, while the meniscus-cartilage phenotype exhibits severe meniscal and cartilage damage. Large bone marrow lesions characterize the subchondral bone phenotype. The hypertrophic and atrophic OA phenotype are defined based on the presence large osteophytes or absence of any osteophytes, respectively, in the presence of concomitant cartilage damage. Limitations of the concept of structural phenotyping are that they are not mutually exclusive and that more than one phenotype may be present. It must be acknowledged that a wide range of views exist on how best to operationalize the concept of structural OA phenotypes and that the concept of structural phenotypic characterization is still in its infancy. Structural phenotypic stratification, however, may result in more targeted trial populations with successful outcomes and practitioners need to be aware of the heterogeneity of the disease to personalize their treatment recommendations for an individual patient. Radiologists should be able to define a joint at risk for progression based on the predominant phenotype present at different disease stages.

Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions

Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).

Strategic application of imaging in DMOAD clinical trials: focus on eligibility, drug delivery, and semiquantitative assessment of structural progression

Despite decades of research efforts and multiple clinical trials aimed at discovering efficacious disease-modifying osteoarthritis (OA) drugs (DMOAD), we still do not have a drug that shows convincing scientific evidence to be approved as an effective DMOAD. It has been suggested these DMOAD clinical trials were in part unsuccessful since eligibility criteria and imaging-based outcome evaluation were solely based on conventional radiography. The OA research community has been aware of the limitations of conventional radiography being used as a primary imaging modality for eligibility and efficacy assessment in DMOAD trials. An imaging modality for DMOAD trials should be able to depict soft tissue and osseous pathologies that are relevant to OA disease progression and clinical manifestations of OA. Magnetic resonance imaging (MRI) fulfills these criteria and advances in technology and increasing knowledge regarding imaging outcomes likely should play a more prominent role in DMOAD clinical trials. In this perspective article, we will describe MRI-based tools and analytic methods that can be applied to DMOAD clinical trials with a particular emphasis on knee OA. MRI should be the modality of choice for eligibility screening and outcome assessment. Optimal MRI pulse sequences must be chosen to visualize specific features of OA.

Latest advancements in imaging techniques in OA

The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.