MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES)

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective

Objective

Knee osteoarthritis (OA) is a disease of the whole joint involving multiple tissue types. MRI-based semi-quantitative (SQ) scoring of knee OA is a method to perform multi-tissue joint assessment and has been shown to be a valid and reliable way to measure structural multi-tissue involvement and progression of the disease. While recent work has described how SQ scoring may be used for clinical trial enrichment and disease phenotyping in OA, less guidance is available for how these parameters may be used to assess study outcomes.

Design

Here we present recommendations for summarizing disease progression within specific tissue types. We illustrate how various methods may be used to quantify longitudinal change using SQ scoring and review examples from the literature.

Results

Approaches to quantify longitudinal change across subregions include the count of number of subregions, delta-subregion, delta-sum, and maximum grade changes. Careful attention should be paid to features that may fluctuate, such as bone marrow lesions, or with certain interventions, for example pharmacologic interventions with anticipated cartilage anabolic effects. The statistical approach must align with the nature of the outcome.

Conclusions

SQ scoring presents a way to understand disease progression across the whole joint. As OA is increasingly recognized as a heterogeneous disease with different phenotypes a better understanding of longitudinal progression across tissue types may present an opportunity to match study outcome to patient phenotype or to treatment mechanism of action.

A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future

OBJECTIVE

This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years.

METHODS

Authors selected representative articles from a PubMed search to illustrate key steps in SQ MRI development, validation, and application. Topics include main scoring systems, reading techniques, responsiveness, reliability, technical considerations, and potential impact of artificial intelligence (AI).

RESULTS

Based on original research published between 1993 and 2023, this article introduces available scoring systems, including but not limited to Whole-Organ Magnetic Resonance Imaging Score (WORMS) as the first system for whole-organ assessment of the knee and the now commonly used MRI Osteoarthritis Knee Score (MOAKS) instrument. Specific systems for distinct OA subtypes or applications have been developed as well as MRI scoring instruments for other joints such as the hip, the fingers or thumb base. SQ assessment has proven to be valid, reliable, and responsive, aiding OA investigators in understanding the natural history of the disease and helping to detect response to treatment. AI may aid phenotypic characterization in the future. SQ MRI assessment's role is increasing in eligibility and safety evaluation in knee OA clinical trials.

CONCLUSIONS

Evidence supports the validity, reliability, and responsiveness of SQ MRI assessment in understanding structural aspects of disease onset and progression. SQ scoring has helped explain associations between structural tissue damage and clinical manifestations, as well as disease progression. While AI may support human readers to more efficiently perform SQ assessment in the future, its current application in clinical trials still requires validation and regulatory approval.

The osteoarthritis prevention study (TOPS) - A randomized controlled trial of diet and exercise to prevent Knee Osteoarthritis: Design and rationale

Background

Osteoarthritis (OA), the leading cause of disability among adults, has no cure and is associated with significant comorbidities. The premise of this randomized clinical trial is that, in a population at risk, a 48-month program of dietary weight loss and exercise will result in less incident structural knee OA compared to control.

Methods/design

The Osteoarthritis Prevention Study (TOPS) is a Phase III, assessor-blinded, 48-month, parallel 2 arm, multicenter randomized clinical trial designed to reduce the incidence of structural knee OA. The study objective is to assess the effects of a dietary weight loss, exercise, and weight-loss maintenance program in preventing the development of structural knee OA in females at risk for the disease. TOPS will recruit 1230 ambulatory, community dwelling females with obesity (Body Mass Index (BMI) ​≥ ​30 ​kg/m2) and aged ≥50 years with no radiographic (Kellgren-Lawrence grade ≤1) and no magnetic resonance imaging (MRI) evidence of OA in the eligible knee, with no or infrequent knee pain. Incident structural knee OA (defined as tibiofemoral and/or patellofemoral OA on MRI) assessed at 48-months from intervention initiation using the MRI Osteoarthritis Knee Score (MOAKS) is the primary outcome. Secondary outcomes include knee pain, 6-min walk distance, health-related quality of life, knee joint loading during gait, inflammatory biomarkers, and self-efficacy. Cost effectiveness and budgetary impact analyses will determine the value and affordability of this intervention.

Discussion

This study will assess the efficacy and cost effectiveness of a dietary weight loss, exercise, and weight-loss maintenance program designed to reduce incident knee OA.

Trial registration

ClinicalTrials.gov Identifier: NCT05946044.

Osteoarthritis year in review 2023: Epidemiology & therapy

OBJECTIVE

To highlight some important findings from osteoarthritis (OA) epidemiology and therapy research undertaken over the past year.

METHODS

Search of MEDLINE and EMBASE databases between April 1, 2022 to March 3, 2023 using "exp *Osteoarthritis/" as the preliminary search term. The search was limited to articles published in English and including human subjects. Final inclusions were based on perceived importance and results that may inform improved identification of risk factors or OA treatments, as well as OA subgroups of potential relevance to risk factors or treatment approaches.

RESULTS

3182 studies were screened, leaving 208 eligible for inclusion. This narrative review of thirty-three selected studies was arranged into: a) OA predictors - population-based studies, b) Specific predictors of OA and OA outcome; c) Intra-articular injections, and d) OA phenotypes. There was some suggestion of sex differences in predictors of incidence or outcomes. Body mass index changes appear largely to affect knee OA outcomes. Evidence points to a lack of benefit of viscosupplementation in knee OA; findings were variable for other injectables. Studies of OA phenotypes reveal potentially relevant clinical and pathophysiological differences.

CONCLUSIONS

Identifying risk factors for the incidence/progression of OA represents an ongoing and important area of OA research. Sex may play a role in this understanding and bears consideration and further study. For knee injectables other than viscosupplementation, additional high-quality trials appear warranted. Continued investigation and application of phenotyping across the OA disease, illness and care spectrum may be key to developing disease-modifying agents and their appropriate selection for individuals.

Imaging Biomarkers of Osteoarthritis

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.

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.

Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification

Background

Osteoarthritis (OA) is a global healthcare problem. The increasing population of OA patients demands a greater bandwidth of imaging and diagnostics. It is important to provide automatic and objective diagnostic techniques to address this challenge. This study demonstrates the utility of unsupervised domain adaptation (UDA) for automated OA phenotype classification.

Methods

We collected 318 and 960 three-dimensional double-echo steady-state magnetic resonance images from the Osteoarthritis Initiative (OAI) dataset as the source dataset for phenotype cartilage/meniscus and subchondral bone, respectively. Fifty three-dimensional turbo spin echo (TSE)/fast spin echo (FSE) MR images from our institute were collected as the target datasets. For each patient, the degree of knee OA was initially graded according to the MRI Knee Osteoarthritis Knee Score before being converted to binary OA phenotype labels. The proposed four-step UDA pipeline included (I) pre-processing, which involved automatic segmentation and region-of-interest cropping; (II) source classifier training, which involved pre-training a convolutional neural network (CNN) encoder for phenotype classification using the source dataset; (III) target encoder adaptation, which involved unsupervised adjustment of the source encoder to the target encoder using both the source and target datasets; and (IV) target classifier validation, which involved statistical analysis of the classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. We compared our model on the target data with the source pre-trained model and the model trained with the target data from scratch.

Results

For phenotype cartilage/meniscus, our model has the best performance out of the three models, giving 0.90 [95% confidence interval (CI): 0.79-1.02] of the AUROC score, while the other two model show 0.52 (95% CI: 0.13-0.90) and 0.76 (95% CI: 0.53-0.98). For phenotype subchondral bone, our model gave 0.75 (95% CI: 0.56-0.94) at AUROC, which has a close performance of the source pre-trained model (0.76, 95% CI: 0.55-0.98), and better than the model trained from scratch on the target dataset only (0.53, 95% CI: 0.33-0.73).

Conclusions

By utilising a large, high-quality source dataset for training, the proposed UDA approach enhances the performance of automated OA phenotype classification for small target datasets. As a result, our technique enables improved downstream analysis of locally collected datasets with a small sample size.

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.

A call for screening MRI as a tool for osteoarthritis clinical trials

Conventional radiography is the most commonly used imaging modality for the evaluation of osteoarthritis (OA) in clinical trials of disease-modifying OA drugs (DMOADs). Unfortunately, radiography has many shortcomings as an imaging technique to meaningfully assess the pathological features of OA. In this perspective paper, we will describe the reasons why radiography is not an ideal tool for structural OA assessment and why magnetic resonance imaging (MRI) should be preferred for such purposes. These shortcomings include a lack of reproducibility of radiographic joint space measurements (if conducted without using a standardized positioning frame), a lack of sensitivity and specificity, an insufficient definition of disease severity, a weak association of radiographic structural damage and pain, a lack of ability to depict many faces of OA, and incapability to depict diagnoses of exclusion. MRI offers solutions to these limitations of radiography. Several different phenotypes of OA have been recognized and it is important to recruit appropriate patients for specific therapeutic approaches in DMOAD trials. Radiography does not allow such phenotypical stratification. We will explain known hurdles for widespread deployment of MRI at eligibility screening and how they can be overcome by technological advances and the use of simplified image assessment.

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).

Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis

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.

Update on recent developments in imaging of inflammation in osteoarthritis: a narrative review

Synovitis is an important component of the osteoarthritis (OA) disease process, particularly regarding the "inflammatory phenotype" of OA. Imaging plays an important role in the assessment of synovitis in OA with MRI and ultrasound being the most deployed imaging modalities. Contrast-enhanced (CE) MRI, particularly dynamic CEMRI (DCEMRI) is the ideal method for synovitis assessment, but for several reasons CEMRI is not commonly performed for OA imaging in general. Effusion-synovitis and Hoffa-synovitis are commonly used as surrogate markers of synovitis on non-contrast-enhanced (NCE) MRI and have been used in many epidemiological observational studies of knee OA. Several semiquantitative MRI scoring systems are available for the evaluation of synovitis in knee OA. Synovitis can be a target tissue for disease-modifying OA drug (DMOAD) clinical trials. Both MRI and ultrasound may be used to determine the eligibility and assess the therapeutic efficacy of DMOAD approaches. Ultrasound is mostly used for evaluation of synovitis in hand OA, while MRI is typically used for larger joints, namely knees and hips. The role of other modalities such as CT (including dual-energy CT) and nuclear medicine imaging (such as positron-emission tomography (PET) and its hybrid imaging) is limited in the context of synovitis assessment in OA. Despite research efforts to develop NCEMRI-based synovitis evaluation methods, these typically underestimate the severity of synovitis compared to CEMRI, and thus more research is needed before we can rely only on NCEMRI.

MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook

Osteoarthritis (OA) is a common degenerative joint disease that affects millions of people worldwide. Magnetic resonance imaging (MRI) has emerged as a powerful tool for the evaluation and monitoring of OA due to its ability to visualize soft tissues and bone with high resolution. This review aims to provide an overview of the current state of MRI in OA, with a special focus on the knee, including protocol recommendations for clinical and research settings. Furthermore, new developments in the field of musculoskeletal MRI are highlighted in this review. These include compositional MRI techniques, such as T2 mapping and T1rho imaging, which can provide additional important information about the biochemical composition of cartilage and other joint tissues. In addition, this review discusses semiquantitative joint assessment based on MRI findings, which is a widely used method for evaluating OA severity and progression in the knee. We analyze the most common scoring methods and discuss potential benefits. Techniques to reduce acquisition times and the potential impact of deep learning in MR imaging for OA are also discussed, as these technological advances may impact clinical routine in the future.

DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative

Background

No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting.

Methods

A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24-48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24-48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric.

Results

The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585-0.743) at baseline, 0.739 (95% CI: 0.703-0.775) at 12 months, and 0.775 (95% CI: 0.686-0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees.

Conclusions

This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

The role of imaging in osteoarthritis

Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.

Personalized Risk Model and Leveraging of Magnetic Resonance Imaging-Based Structural Phenotypes and Clinical Factors to Predict Incidence of Radiographic Osteoarthritis

OBJECTIVE

Our study aimed to investigate the association between time to incidence of radiographic osteoarthritis (OA) and magnetic resonance imaging (MRI)-based structural phenotypes proposed by the Rapid Osteoarthritis MRI Eligibility Score (ROAMES).

METHODS

A retrospective cohort of 2,328 participants without radiographic OA at baseline were selected from the Osteoarthritis Initiative study. Utilizing a deep-learning model, we automatically assessed the presence of inflammatory, meniscus/cartilage, subchondral bone, and hypertrophic phenotypes from MRIs acquired at baseline and 12-, 24-, 36-, 48-, 72-, and 96-month follow-up visits. In addition to 4 structural phenotypes, we examined severe knee injury history and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores as time dependent. We used Cox proportional hazards regression to analyze the association between 4 structural phenotypes and radiographic OA disease-free survival, both univariate and adjusted for known risk factors including age, sex, race, body mass index, presence of Heberden's nodes, and knee malalignment.

RESULTS

Inflammatory (hazard ratio [HR] 3.37 [95% confidence interval (95% CI) 2.45-4.63]), meniscus/cartilage (HR 1.55 [95% CI 1.21-1.98]), and subchondral bone (HR 1.84 [95% CI 1.63-2.09]) phenotypes were associated with time to radiographic OA at P < 0.05 when adjusted for the risk factors. Sex was a modifier of hypertrophic phenotype association with time to radiographic OA. Female participants with the hypertrophic phenotype were associated with 2.8 times higher risk of radiographic OA (95% CI 2.25-7.54) compared to male participants without the hypertrophic phenotype.

CONCLUSION

Four ROAMES phenotypes may contribute to time to radiographic OA incidence and if validated could be used as a promising tool for personalized OA management.

Imaging of Rheumatic Diseases Affecting the Lower Limb

Imaging methods capable of detecting inflammation, such as MR imaging and ultrasound, are of paramount importance in rheumatic disease management, not only for diagnostic purposes but also for monitoring disease activity and treatment response. However, more advanced stages of arthritis, characterized by findings of cumulative structural damage, have traditionally been accomplished by radiographs and computed tomography. The purpose of this review is to provide an overview of imaging of some of the most prevalent inflammatory rheumatic diseases affecting the lower limb (osteoarthritis, rheumatoid arthritis, and gout) and up-to-date recommendations regarding imaging diagnostic workup.

MRI underestimates presence and size of knee osteophytes using CT as a reference standard

OBJECTIVE

To explore the diagnostic performance of routine magnetic resonance imaging (MRI) for the cross-sectional assessment of osteophytes (OPs) in all three knee compartments using computed tomography (CT) as a reference standard.

METHODS

The Strontium Ranelate Efficacy in Knee Osteoarthritis (SEKOIA) trial explored the effect of 3 years of treatment with strontium ranelate in patients with primary knee OA. OPs were scored for the baseline visit only using a modified MRI Osteoarthritis Knee Score (MOAKS) scoring system in the patellofemoral (PFJ), the medial tibiofemoral (TFJ) and the lateral TFJ. Size was assessed from 0 to 3 in 18 locations. Descriptive statistics were used to describe differences in ordinal grading between CT and MRI. In addition, weighted-kappa statistics were employed to assess agreement between scoring using the two methods. Sensitivity, specificity, positive predictive value and negative predictive value as well as area under the curve (AUC) measures of diagnostic performance were employed using CT as the reference standard.

RESULTS

Included were 74 patients with available MRI and CT data. Mean age was 62.9 ± 7.5 years. Altogether 1,332 locations were evaluated. For the PFJ, MRI detected 141 (72%) of 197 CT-defined OPs with a w-kappa of 0.58 (95% CI [0.52-0.65]). In the medial TFJ, MRI detected 178 (81%) of 219 CT-OPs with a w-kappa of 0.58 (95% CI [0.51-0.64]). For the lateral compartment these numbers were 84 (70%) of 120 CT-OPs with a w-kappa of 0.58 (95% CI [0.50-0.66]).

CONCLUSION

MRI underestimates presence of osteophytes in all three knee compartments. CT may be helpful particularly regarding assessment of small osteophytes particularly in early disease.

Osteoarthritis Bone Marrow Lesions

Assessment and treatment of Bone Marrow Lesions (BMLs) could ultimately make step changes to the lives of people with osteoarthritis (OA). We here review the imaging and pathological characteristics of OA-BMLs, their differential diagnosis and measurement, and cross-sectional and longitudinal associations with pain and OA structural progression. We discuss how biomechanical and cellular factors may contribute to BML pathogenesis, and how pharmacological and non-pharmacological interventions that target BMLs might reduce pain and OA structural progression. We critically appraise semiquantitative and quantitative methods for assessing BMLs, and their potential utilities for identifying people at risk of symptomatic and structural OA progression, and evaluating treatment responses. New interventions that target OA-BMLs should both confirm their importance, and reduce the unacceptable burden of OA.

Advances in osteoarthritis imaging

PURPOSE OF REVIEW

Imaging plays a pivotal role for diagnosis, follow-up and stratification of osteoarthritis patients in clinical trials and research. We aim to present an overview of currently available and emerging imaging techniques for osteoarthritis assessment and provide insight into relevant benefits and pitfalls of the different modalities.

RECENT FINDINGS

Although radiography is considered sufficient for a structural diagnosis of osteoarthritis and is commonly used to define eligibility of patients for participation in clinical trials, it has inherent limitations based on the projectional nature of the technique and inherent challenges regarding reproducibility in longitudinal assessment. MRI has changed our understanding of the disease from 'wear and tear' of cartilage to a whole organ disorder. MRI assessment of structural changes of osteoarthritis includes semi-quantitative, quantitative and compositional evaluation. Ultrasound is helpful in evaluating the degree of synovitis and has value in the assessment particularly of the patella-femoral joint. Recent development of computed tomography technology including weight-bearing systems has led to broader application of this technology in a research context.

SUMMARY

Advances in MRI technology have resulted in a significant improvement in understanding osteoarthritis as a multitissue disease.

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.

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis

PURPOSE

To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning.

METHODS

The sagittal tibial tubercle-trochlear groove (sTTTG) distance, tibial tubercle-trochlear groove distance, trochlear sulcus angle, trochlear depth, Caton-Deschamps Index (CDI), and flexion angle were measured by use of deep learning-generated segmentations on a subset of the Osteoarthritis Initiative study with radiologist-graded PFJ cartilage grades (n = 2,461). Kruskal-Wallis H tests were performed to compare differences in PFJ morphology between subjects without PFJ osteoarthritis (OA) and those with PFJ OA. PFJ morphology was correlated with secondary outcomes of mean patellar cartilage thickness and mean patellar cartilage T2 relaxation time using linear regression models controlling for age, sex, and body mass index.

RESULTS

A total of 1,626 knees did not have PFJ OA, whereas 835 knees had PFJ OA. Knees without PFJ OA had an increased (anterior) sTTTG distance (mean ± standard deviation, 11.1 ± 12.8 mm) compared with knees with PFJ OA (8.4 ± 12.7 mm) (P < .001), indicating a more posterior tibial tubercle in subjects with PFJ OA. Knees without PFJ OA had a decreased sulcus angle (127.4° ± 7.1° vs 128.0° ± 8.4°, P = .01) and increased trochlear depth (9.1 ± 1.7 mm vs 9.0 ± 2.0 mm, P = .03) compared with knees with PFJ OA. Decreased patellar cartilage thickness was associated with decreased trochlear depth (β = 0.12, P = .002) and increased CDI (β = -0.07, P < .001). Increased patellar cartilage T2 relaxation time was correlated with decreased sTTTG distance (β = -0.08, P = .01), decreased sulcus angle (β = -0.12, P = .04), and decreased CDI (β = -0.12, P < .001).

CONCLUSIONS

PFJ OA, patellar cartilage thickness, and patellar cartilage T2 relaxation time were shown to be associated with the underlying geometries within the PFJ. This large longitudinal study highlights that a decreased sTTTG distance (i.e., a more posterior tibial tubercle) is significantly associated with PFJ degenerative cartilage change.

LEVEL OF EVIDENCE

Level III, retrospective comparative prognostic trial.

Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort

Background

The IMI-APPROACH cohort is an exploratory, 5-centre, 2-year prospective follow-up study of knee osteoarthritis (OA). Aim was to describe baseline multi-tissue semiquantitative MRI evaluation of index knees and to describe change for different MRI features based on number of subregion-approaches and change in maximum grades over a 24-month period.

Methods

MRIs were acquired using 1.5 T or 3 T MRI systems and assessed using the semi-quantitative MRI OA Knee Scoring (MOAKS) system. MRIs were read at baseline and 24-months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. In descriptive fashion, the frequencies of MRI features at baseline and change in these imaging biomarkers over time are presented for the entire sample in a subregional and maximum score approach for most features. Differences between knees without and with structural radiographic (R) OA are analyzed in addition.

Results

Two hundred eighty-nine participants had readable baseline MRI examinations. Mean age was 66.6 ± 7.1 years and participants had a mean BMI of 28.1 ± 5.3 kg/m2. The majority (55.3%) of included knees had radiographic OA. Any change in total cartilage MOAKS score was observed in 53.1% considering full-grade changes only, and in 73.9% including full-grade and within-grade changes. Any medial cartilage progression was seen in 23.9% and any lateral progression on 22.1%. While for the medial and lateral compartments numbers of subregions with improvement and worsening of BMLs were very similar, for the PFJ more improvement was observed compared to worsening (15.5% vs. 9.0%). Including within grade changes, the number of knees showing BML worsening increased from 42.2% to 55.6%. While for some features 24-months change was rare, frequency of change was much more common in knees with vs. without ROA (e.g. worsening of total MOAKS score cartilage in 68.4% of ROA knees vs. 36.7% of no-ROA knees, and 60.7% vs. 21.8% for an increase in maximum BML score per knee).

Conclusions

A wide range of MRI-detected structural pathologies was present in the IMI-APPROACH cohort. Baseline prevalence and change of features was substantially more common in the ROA subgroup compared to the knees without ROA.

Trial Registration

Clinicaltrials.gov identification: NCT03883568.

FDA/Arthritis Foundation osteoarthritis drug development workshop recap: Assessment of long-term benefit

OBJECTIVE

To summarize proceedings of a workshop convened to discuss the current state of science in the disease of osteoarthritis (OA), identify the knowledge gaps, and examine the developmental and regulatory challenges in bringing these products to market.

DESIGN

Summary of the one-day workshop held virtually on June 22nd, 2021.

RESULTS

Speakers selected by the Planning Committee presented data on the current approach to assessment of OA therapies, biomarkers in OA drug development, and the assessment of disease progression and long-term benefit.

CONCLUSIONS

Demonstrated by numerous failed clinical trials, OA is a challenging disease for which to develop therapeutics. The challenge is magnified by the slow time of onset of disease and the need for clinical trials of long duration and/or large sample size to demonstrate the effect of an intervention. The OA science community, including academia, pharmaceutical companies, regulatory agencies, and patient communities, must continue to develop and test better clinical endpoints that meaningfully reflect disease modification related to long-term patient benefit.

Imaging in Osteoarthritis

Osteoarthritis (OA) is the most frequent form of arthritis with major implications on both individual and public health care levels. The field of joint imaging, and particularly magnetic resonance imaging (MRI), has evolved rapidly due to the application of technical advances to the field of clinical research. This narrative review will provide an introduction to the different aspects of OA imaging aimed at an audience of scientists, clinicians, students, industry employees, and others who are interested in OA but who do not necessarily focus on OA. The current role of radiography and recent advances in measuring joint space width will be discussed. The status of cartilage morphology assessment and evaluation of cartilage biochemical composition will be presented. Advances in quantitative three-dimensional morphologic cartilage assessment and semi-quantitative whole-organ assessment of OA will be reviewed. Although MRI has evolved as the most important imaging method used in OA research, other modalities such as ultrasound, computed tomography, and metabolic imaging play a complementary role and will also be discussed.

Imaging of Osteoarthritis of the Knee

Knee osteoarthritis is rising in prevalence, and more imaging studies are being requested to evaluate these patients. Although conventional radiographs of the knee are the most widely requested and available studies, other imaging modalities such as MRI, CT, and ultrasound may also be used. This article reviews commonly used imaging modalities, advantages and limitations of each, and their clinical applicability in diagnosing and monitoring knee osteoarthritis. New and advanced imaging techniques are also discussed as possible methods of early diagnosis and improved understanding of osteoarthritis pathophysiology.

Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging

The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis research, by facilitating the elucidation of disease pathogenesis and progression. The recent increasing employment of MRI has certainly been stimulated by major advances that are due to considerable investment in research, particularly related to artificial intelligence (AI). These AI-related advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times of MRI, conducting large-scale longitudinal studies and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field to improve understanding of osteoarthritis. Compared with labour-intensive human efforts, AI-based methods have advantages and potential in all stages of imaging, as well as post-processing steps, including aiding diagnosis and prognosis. However, AI-based methods also have limitations, including the arguably limited interpretability of AI models. Given that the AI community is highly invested in uncovering uncertainties associated with model predictions and improving their interpretability, we envision future clinical translation and progressive increase in the use of AI algorithms to support clinicians in optimizing patient care.

Osteoarthritis year in review 2021: imaging

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.

How to effectively utilize imaging in disease-modifying treatments for osteoarthritis clinical trials: the radiologist's perspective

Introduction: One of the reasons for failures of disease-modifying osteoarthritis drug clinical trials has been the radiography-based definition of structural eligibility criteria. Imaging, particularly MRI, has a critical role in planning and conducting clinical trials of osteoarthritis.Areas covered: A literature search was performed using keywords including 'osteoarthritis,' 'knee,' 'MRI,' 'intra-articular injection,' 'semiquantitative scoring,' 'clinical trial,' and other specific terms where relevant. The core concepts of using MRI in osteoarthritis clinical trials are explained focusing on knee osteoarthritis, including its role in determining patient eligibility and inclusion/exclusion criteria as well as outcome measures from the expert musculoskeletal radiologist's perspective. A brief overview of statistical analyses that should be deployed in clinical trials utilizing semiquantitative MRI analyses is discussed.Expert opinion: In order to increase chances to detect measurable efficacy effects, investigators should consider utilizing MRI from screening to outcome assessment. Recognition of several phenotypes of osteoarthritis helps in participant stratification and will lead to more targeted clinical trials. Inclusion and exclusion criteria need to be defined using not only radiography but also MRI. Correct intra-articular injection of investigational compounds is critically important if intra-articular drug delivery is required, and such procedure should be performed and documented using appropriate imaging guidance.

Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis

Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.

Setting up distinctive outcome measures for each osteoarthritis phenotype

Osteoarthritis (OA) is an evolving chronic joint disease with a huge global impact. Given the intricate nature of the etiopathogenesis and subsequent high heterogeneity in the clinical course of OA, it is crucial to discriminate between etiopathogenic endotypes and clinical phenotypes, especially in the early stages of the disease. In this sense, we propose that an OA phenotype should be properly assessed with a set of outcome measures including those specifically related to the main underlying pathophysiological mechanisms. Thus, each OA phenotype can be related to different and clinically meaningful outcomes. OA phenotyping would lead to an adequate patient stratification in well-designed clinical trials and the discovery of precise therapeutic approaches. A significant effort will be required in this field in light of inconclusive results of clinical trials of tissue-targeting agents for the treatment of OA.

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)

PURPOSE

Aim was to stratify the knee MRIs of the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium (FNIH) cohort into distinct structural phenotypes based on semiquantitative assessment and to determine risk for pain and structural progression over 48 months.

METHODS

The study sample from the FNIH project was selected as a nested case-control study with knees showing either 1) radiographic and pain progression (i.e., "composite" cases), 2) radiographic progression only ("JSL"), 3) pain progression only, and 4) neither radiographic nor pain progression. MRI was performed on 3T systems. MRIs were read according to the MOAKS scoring system. Knees were stratified into subchondral bone, cartilage/meniscus and inflammatory phenotypes using the baseline visits. The relation of each phenotype to risk of being in the combined JSL plus composite outcome or composite case only group compared to those not having that phenotype was determined using logistic regression. Only KL2 and 3 and those without root tears were included.

RESULTS

485 knees were included. 362 (75%) did not have any phenotype, while 95 (20%) had the bone phenotype, 22 (5%) the cartilage/meniscus phenotype and 19 (4%) the inflammatory phenotype. The bone phenotype was associated with a higher odds of the combined JSL plus composite outcome and composite outcome only (OR 1.81; [95%CI 1.14,2.85] and 1.65; 95%CI [1.04,2.61]) while the inflammatory (OR 0.96 [95%CI 0.38,2.42] and 1.25; 95%CI [0.48,3.25]) and the cartilage/meniscus phenotypes were not significantly associated with outcome (OR 1.30 95%CI [0.55,3.07] and 0.99; 95%CI [0.40,2,49]).

CONCLUSIONS

The bone phenotype was associated with increased risk of having both radiographic and pain progression. Phenotypic stratification may be useful to consider when selecting patients for inclusion in clinical trials.

Development and Prospect of Intra-Articular Injection in the Treatment of Osteoarthritis: A Review

Osteoarthritis (OA) is a common degenerative disease that affects the vast majority of the elderly and may eventually embark on the road of the total knee arthroplasty (TKA), although controversy still exists in the medical community about the best therapies for osteoarthritis. Compared with physical therapy, oral analgesics and other non-operative treatments, intra-articular injection is more safe and effective. Moreover, intra-articular injection is much less invasive and has fewer adverse reactions than surgical treatment. This article reviews mechanism, benefits and adverse reactions of corticosteroids (CS), hyaluronic acid (HA), platelet-rich plasma (PRP), mesenchymal stem cell (MSCs), stromal vascular fraction (SVF) and other new therapies (for example: gene therapy). The application prospect of intra-articular injection was analyzed according to the recent progress in drug research.

Pharmacodynamics, efficacy, safety and administration of intra-articular therapies for knee osteoarthritis

Introduction: Osteoarthritis (OA) is the leading cause of disability in the elderly, usually presenting with mono-or oligo-arthritis where local drug delivery by intra-articular (IA) injection may be more effective in terms of increased bioavailability, less systemic exposure and reduced adverse events. Several intra-articular medications for symptomatic are available on the market while the new disease-modifying drugs (DMOADs) are progressing into phase 3 pipeline of drug development.Areas covered: This narrative review covered the pharmacokinetic and pharmacodynamics of clinically available IA drugs which include corticosteroids, hyaluronic acid as well as injection techniques, efficacy, adverse effects and contraindications. In addition, the authors briefly describe the newer disease-modifying OA drugs (DMOAD) which are undergoing phase 3 pipeline of development such as Fibroblast growth factor (FGF-18) and Wnt inhibitor.Expert opinion: This is a rapidly evolving area with both new products and new trials regularly emerging. It is also a critically important area in a disease field that lacks for safe and effective treatments, where intra-articular delivery may enhance both local efficacy and reduce systemic toxicity.