Imaging research results from the osteoarthritis initiative (OAI): a review and lessons learned 10 years after start of enrolment

30 Years of MRI-based cartilage & bone morphometry in knee osteoarthritis: From correlation to clinical trials

OBJECTIVE

The first publication on morphometric analysis of articular cartilage using magnetic resonance imaging (MRI) in 1994 set the scene for a game change in osteoarthritis (OA) research. The current review highlights milestones in cartilage and bone morphometry, summarizing the rapid progress made in imaging, its application to understanding joint (patho-)physiology, and its use in interventional clinical trials.

METHODS

Based on a Pubmed search of articles from 1994 to 2023, the authors subjectively selected representative work illustrating important steps in the development or application of magnetic resonance-based cartilage and bone morphometry, with a focus on studies in humans, and on the knee. Research on OA-pathophysiology is addressed only briefly, given length constraints. Compositional and semi-quantitative assessment are not covered here.

RESULTS

The selected articles are presented in historical order as well as by content. We review progress in the technical aspects of image acquisition, segmentation and analysis, advances in understanding tissue growth, physiology, function, and adaptation, and a selection of clinical trials examining the efficacy of interventions on knee cartilage and bone. A perspective is provided of how lessons learned may be applied to future research and clinical management.

CONCLUSIONS

Over the past 30 years, MRI-based morphometry of cartilage and bone has contributed to a paradigm shift in understanding articular tissue physiology and OA pathophysiology, and to the development of new treatment strategies. It is likely that these technologies will continue to play a key role in the development and (accelerated) approval of therapy, potentially targeted to different OA phenotypes.

Automated Radiographic Measurements of Knee Osteoarthritis

OBJECTIVE

Herewith, we report the development of Orthopedic Digital Image Analysis (ODIA) software that is developed to obtain quantitative measurements of knee osteoarthritis (OA) radiographs automatically. Manual segmentation and measurement of OA parameters currently hamper large-cohort analyses, and therefore, automated and reproducible methods are a valuable addition in OA research. This study aims to test the automated ODIA measurements and compare them with available manual Knee Imaging Digital Analysis (KIDA) measurements as comparison.

DESIGN

This study included data from the CHECK (Cohort Hip and Cohort Knee) initiative, a prospective multicentre cohort study in the Netherlands with 1,002 participants. Knee radiographs obtained at baseline of the CHECK cohort were included and mean medial/lateral joint space width (JSW), minimal JSW, joint line convergence angle (JLCA), eminence heights, and subchondral bone intensities were compared between ODIA and KIDA.

RESULTS

Of the potential 2,004 radiographs, 1,743 were included for analyses. Poor intraclass correlation coefficients (ICCs) were reported for the JLCA (0.422) and minimal JSW (0.299). The mean medial and lateral JSW, eminence height, and subchondral bone intensities reported a moderate to good ICC (0.7 or higher). Discrepancies in JLCA and minimal JSW between the 2 methods were mostly a problem in the lateral tibia plateau.

CONCLUSIONS

The current ODIA tool provides important measurements of OA parameters in an automated manner from standard radiographs of the knee. Given the automated and computerized methodology that has very high reproducibility, ODIA is suitable for large epidemiological cohorts with various follow-up time points to investigate structural progression, such as CHECK or the Osteoarthritis Initiative (OAI).

Fewer Than One in 20 Current Academic Orthopaedic Surgeons Have Obtained National Institutes of Health Funding

BACKGROUND

National Institutes of Health (NIH) funding is a key driver of orthopaedic research, but it has become increasingly difficult to obtain in recent years. An understanding of the types of grants that are commonly funded, how productive they are, and the factors associated with obtaining funding may help orthopaedic surgeons better understand how to earn grants.

QUESTIONS/PURPOSES

In this study, we sought to determine (1) the proportion of current academic orthopaedic surgeons who have obtained NIH grant funding, (2) the productivity of these grants by calculating grant productivity metrics, and (3) the factors (such as gender, subspecialty, and additional degrees) that are associated with obtaining grant funding.

METHODS

Current academic orthopaedic surgeons at the top 140 NIH-funded institutions were identified via faculty webpages; 3829 surgeons were identified. Demographic information including gender (men constituted 88% of the group [3364 of 3829]), academic rank (full professors constituted 22% [856 of 3829]), additional degrees (those with MD-PhD degrees constituted 3% [121 of 3829]), leadership positions, and orthopaedic subspecialty was collected. Funding histories from 1985 through 2021 were collected using the NIH Research Portfolio Online Reporting Tools Expenditures and Results. Grant type, funding, publications, and citations of each article were collected. A previously used grant impact metric (total citations per USD 0.1 million) was calculated to assess grant productivity. Multivariable binomial logistic regression was used to evaluate factors associated with obtaining funding.

RESULTS

Four percent (150 of 3829) of academic orthopaedic surgeons obtained USD 338.3 million in funding across 301 grants, resulting in 2887 publications over the entire study period. The R01 was the most commonly awarded grant in terms of the total number awarded, at 36% (108 of 301), as well as by funding, publications, and citations, although other grant types including T32, F32, R03, R13, and R21 had higher mean grant impact metrics. There was no difference between men and women in the by-gender percentage of academic orthopaedic surgeons who obtained funding (4% [135 of 3229] versus 3% [15 of 450]; odds ratio 0.9 [95% confidence interval 0.5 to 1.7]; p = 0.80). A department having a single funded PhD researcher may be associated with surgeon-scientists obtaining grant funding, but with the numbers available, we could not demonstrate this was the case (OR 1.4 [95% CI 0.9 to 2.2]; p = 0.12).

CONCLUSION

Fewer than one in 20 academic orthopaedic surgeons have received NIH funding. R01s are the most commonly awarded grant, although others demonstrate increased productivity metrics. Future studies should investigate the role of co-principal investigators on productivity and the role of different funding sources.

CLINICAL RELEVANCE

Individuals should pursue both R01 and non-R01 grants, and departments should consider cultivating relationships with funded PhDs. The specific research infrastructure and departmental policies of the most productive institutions and grants should be surveyed and emulated.

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications

Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state-of-the-art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio-frequency pulse sequences, and deep learning-based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

BACKGROUND

Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized.

PURPOSE

Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population.

STUDY TYPE

Retrospective based on prospectively acquired data.

POPULATION

Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females).

FIELD STRENGTH/SEQUENCE

A 3-T, quantitative double-echo steady state (qDESS).

ASSESSMENT

Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage.

STATISTICAL TESTS

Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant.

RESULTS

DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec.

DATA CONCLUSION

The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

A review of deep learning-based deformable medical image registration

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

Association of Superficial Cartilage Transverse Relaxation Time With Osteoarthritis Disease Progression: Data From the Foundation for the National Institutes of Health Biomarker Study of the Osteoarthritis Initiative

OBJECTIVE

To study whether layer-specific cartilage transverse relaxation time (T2) and/or longitudinal change is associated with clinically relevant knee osteoarthritis (OA) disease progression.

METHODS

The Foundation for the National Institutes of Health Biomarker Consortium was a nested case-control study on 600 knees from 600 Osteoarthritis Initiative participants. Progressor knees had both medial tibiofemoral radiographic joint space width (JSW) loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score (≥9 on a 0-100 scale) at 24-48 months from baseline (n = 194). Multiecho spin-echo (MESE) magnetic resonance images (MRIs) for cartilage T2 analysis had been acquired in the right knees only (97 progressor knees). These were compared to 104 control knees without JSW or pain progression. Fifty-three knees had JSW progression, and 57 pain progression only. Cartilage thickness segmentations obtained from double-echo steady-state MRI were matched to MESE MRI to extract superficial and deep femorotibial cartilage T2. Superficial medial femorotibial compartment (MFTC) T2 at baseline was the primary, and change in deep MFTC T2 between baseline and 12 months was the secondary analytic outcome of this post hoc exploratory study.

RESULTS

Baseline superficial MFTC T2 was significantly elevated in progressor knees (adjusted mean 47.2 msec [95% confidence interval (95% CI) 46.5, 48.0]) and JSW progression only knees (adjusted mean 47.3 msec [95% CI 46.3, 48.3]), respectively, versus non-progressor knees (45.8 msec [95% CI 45.0, 46.5]) after adjustment for age, sex, body mass index, WOMAC pain score, and medial joint space narrowing grade (analysis of covariance). Change in T2 was not significantly associated with case status.

CONCLUSION

Baseline superficial, but not deep, medial cartilage T2 is associated with clinically relevant disease progression in knee OA.

Phenotype-Genotype analysis of caucasian patients with high risk of osteoarthritis

Background: Osteoarthritis (OA) is a common cause of disability and pain around the world. Epidemiologic studies of family history have revealed evidence of genetic influence on OA. Although many efforts have been devoted to exploring genetic biomarkers, the mechanism behind this complex disease remains unclear. The identified genetic risk variants only explain a small proportion of the disease phenotype. Traditional genome-wide association study (GWAS) focuses on radiographic evidence of OA and excludes sex chromosome information in the analysis. However, gender differences in OA are multifactorial, with a higher frequency in women, indicating that the chromosome X plays an essential role in OA pathology. Furthermore, the prevalence of comorbidities among patients with OA is high, indicating multiple diseases share a similar genetic susceptibility to OA. Methods: In this study, we performed GWAS of OA and OA-associated key comorbidities on 3366 OA patient data obtained from the Osteoarthritis Initiative (OAI). We performed Mendelian randomization to identify the possible causal relationship between OA and OA-related clinical features. Results: One significant OA-associated locus rs2305570 was identified through sex-specific genome-wide association. By calculating the LD score, we found OA is positively correlated with heart disease and stroke. A strong genetic correlation was observed between knee OA and inflammatory disease, including eczema, multiple sclerosis, and Crohn's disease. Our study also found that knee alignment is one of the major risk factors in OA development, and we surprisingly found knee pain is not a causative factor of OA, although it was the most common symptom of OA. Conclusion: We investigated several significant positive/negative genetic correlations between OA and common chronic diseases, suggesting substantial genetic overlaps between OA and these traits. The sex-specific association analysis supports the critical role of chromosome X in OA development in females.

Circulating microRNAs differentiate fast-progressing from slow-progressing and non-progressing knee osteoarthritis in the Osteoarthritis Initiative cohort

Introduction:

The objective of this study is to identify circulating microRNAs that distinguish fast-progressing radiographic knee osteoarthritis (OA) in the Osteoarthritis Initiative cohort by applying microRNA-sequencing.

Methods:

Participants with Kellgren–Lawrence (KL) grade 0/1 at baseline were included (N = 106). Fast-progressors were defined by an increase to KL 3/4 by 4-year follow-up (N = 20), whereas slow-progressors showed an increase to KL 2/3/4 only at 8-year follow-up (N = 35). Non-progressors remained at KL 0/1 by 8-year follow-up (N = 51). MicroRNA-sequencing was performed on plasma collected at baseline and 4-year follow-up from the same participants. Negative binomial models were fitted to identify differentially expressed (DE) microRNAs. Penalized logistic regression (PLR) analyses were performed to select combinations of DE microRNAs that distinguished fast-progressors. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate predictive ability.

Results:

DE analyses revealed 48 microRNAs at baseline and 2 microRNAs at 4-year follow-up [false discovery rate (FDR) < 0.05] comparing fast-progressors with both slow-progressors and non-progressors. Among these were hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-320e, which were predicted to target gene families, including members of the 14-3-3 gene family, involved in signal transduction. PLR models included miR-320 members as top predictors of fast-progressors and yielded AUC ranging from 82.6 to 91.9, representing good accuracy.

Conclusion:

The miR-320 family is associated with fast-progressing radiographic knee OA and merits further investigation as potential biomarkers and mechanistic drivers of knee OA.

Degenerative joint disease induced by repeated intra-articular injections of monosodium urate crystals in rats as investigated by translational imaging

The objective of this work was to assess the consequences of repeated intra-articular injection of monosodium urate (MSU) crystals with inflammasome priming by lipopolysaccharide (LPS) in order to simulate recurrent bouts of gout in rats. Translational imaging was applied to simultaneously detect and quantify injury in different areas of the knee joint. MSU/LPS induced joint swelling, synovial membrane thickening, fibrosis of the infrapatellar fat pad, tidemark breaching, and cartilage invasion by inflammatory cells. A higher sensitivity to mechanical stimulus was detected in paws of limbs receiving MSU/LPS compared to saline-injected limbs. In MSU/LPS-challenged joints, magnetic resonance imaging (MRI) revealed increased synovial fluid volume in the posterior region of the joint, alterations in the infrapatellar fat pad reflecting a progressive decrease of fat volume and fibrosis formation, and a significant increase in the relaxation time T2 in femoral cartilage, consistent with a reduction of proteoglycan content. MRI also showed cyst formation in the tibia, femur remodeling, and T2 reductions in extensor muscles consistent with fibrosis development. Repeated intra-articular MSU/LPS injections in the rat knee joint induced pathology in multiple tissues and may be a useful means to investigate the relationship between urate crystal deposition and the development of degenerative joint disease.

Longitudinal Change in Knee Cartilage Thickness and Function in Subjects with and without MRI-Diagnosed Cartilage Damage

OBJECTIVE

Cartilage damage diagnosed by magnetic resonance imaging (MRI) is highly prevalent in the population. In this article, we explore whether such cartilage damage is associated with greater longitudinal change in 3D cartilage thickness and knee function in subjects without (risk factors of) knee osteoarthritis.

DESIGN

Eighty-two knees of Osteoarthritis Initiative healthy reference cohort participants had baseline and 4-year follow-up MRI and knee function data. Baseline presence of semiquantitatively assessed MRI-based cartilage damage (MOAKS [MRI Osteoarthritis Knee Score] ≥ grade 1.0) was recorded by an experienced radiologist. Longitudinal femorotibial cartilage thickness change was determined after segmentation, using location-independent methodology. Knee function was evaluated by patient-reported outcomes and functional performance measures. Statistical comparisons included analysis of covariance adjusting for age, sex, and body mass index.

RESULTS

Forty-five percent of the participants had cartilage damage in at least one femorotibial subregion; the cartilage thickness change score was 15% greater in participants with than in those without damage (1216 ± 434 vs. 1058 ± 277 µm). This difference reached borderline statistical significance with and without adjustment for age, sex, and body mass index (P = 0.05). No significant differences in the change of patient-reported outcomes of knee function (PASE [physical activity score of the elderly] and WOMAC [Western Ontario McMaster Osteoarthritis Index]) or chair stand test results were detected. Of those without femorotibial damage, 58% had cartilage damage in at least one femoropatellar subregion; these had a 9% greater femorotibial cartilage change score than those without femoropatellar or femorotibial damage (difference not statistically significant).

CONCLUSIONS

In the absence of osteoarthritis risk factors, semiquantitatively assessed MRI-based cartilage damage appears to be associated with greater longitudinal location-independent femorotibial cartilage thickness changes, but not with greater functional deteriorations.

Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation

BACKGROUND

Thigh intermuscular (IMF) and subcutaneous (SCF) fat are associated with joint function, inflammation and knee osteoarthritis. Fully automated segmentation from MRI is important to study the above relationship in larger cohorts. However, such algorithms are not clinically evaluated for longitudinal studies. Our aim was to evaluate a fully automated U-Net segmentation approach and its ability to detect longitudinal changes in thigh IMF and SCF during weight changes compared to manual segmentation.

METHODS

103 Osteoarthritis Initiative subjects, were studied, 52 with> 10% weight loss, and 51 with> 10% weight gain over 2-years. Longitudinal change in IMF and SCF were determined from baseline and year-2 axial thigh MRIs using U-Net segmentation. The standardised response mean (SRM) was used as measure of sensitivity to change.

RESULTS

The U-Net took substantially less time (single-slice MRI:< 1 s) and IMF and SCF showed very similar sensitivity to change as manual segmentation: With an average weight gain of + 14%, we observed an + 12% /+ 26% increase in IMF / SCF (SRM=0.99 /1.03) using the U-Net, compared with + 21% /+ 27% (SRM=0.60 /1.07) for manual segmentation. During an average weight loss of - 18%, we observed an - 14% /- 22% reduction in IMF /SCF (SRM = - 1.04 /-1.20) using the U-Net, compared with - 16% /- 22% (SRM = - 0.70 /-1.23) for manual segmentation.

CONCLUSION

U-Net segmentation replicates longitudinal changes of IMF and SCF associated with weight changes with a similar sensitivity to change as manual segmentation. This method is applicable to large databases for studying relationships between IMF and SCF and various disease conditions.

Future Directions in Robotic Neurosurgery

In this paper, we highlight promising technologies in each phase of a robotic neurosurgery operation, and identify key factors affecting how quickly these technologies will mature into products in the operating room. We focus on specific technology trends in image-guided cranial and spinal procedures, including advances in imaging, machine learning, robotics, and novel interfaces. For each technology, we discuss the required effort to overcome safety or implementation challenges, as well as identifying example regulatory approved products in related fields for comparison. The goal is to provide a roadmap for clinicians as to which robotic and automation technologies are in the developmental pipeline, and which ones are likely to impact their practice sooner, rather than later.

Trajectories of Structural Disease Progression in Knee Osteoarthritis

OBJECTIVE

Knee osteoarthritis (OA) is a heterogeneous disease, with most patients experiencing slow disease progression and some with rapid deterioration. We aimed to identify groups of patients with symptomatic knee OA experiencing rapid structural progression.

METHODS

We selected participants from the Osteoarthritis Initiative with baseline Kellgren/Lawrence (K/L) grades 1-3 and knee pain, and with joint space width (JSW) on fixed-flexion knee radiographs assessed at baseline and with ≥1 follow-up over 8 years. We used latent class growth analysis to identify subgroups of JSW progression, jointly modeling time to knee replacement (KR) to account for potential informative dropouts. After identifying trajectories, we used logistic regression to assess the association between baseline characteristics and the JSW trajectory group.

RESULTS

We used data from 1,578 participants. Baseline radiographic severity was K/L grade 1 in 17%, K/L grade 2 in 50%, and K/L grade 3 in 33%. We identified 3 distinct JSW trajectories: 86% stable, 6% with stable JSW followed by late progression, and 8% with early progression. Incorporating information about KR resulted in 47% of KRs initially classified as stable being reclassified to 1 of the progressing trajectories. Prior knee surgery was associated with being in the late-progressing versus the stable trajectory, while obesity was associated with being in the early-progressing versus stable trajectory.

CONCLUSION

In addition to a subgroup of individuals experiencing early structural progression, 8-year longitudinal data allowed the identification of a late-progressing trajectory. Incorporating information about KR was important to properly identify longitudinal structural trajectories in knee OA.

Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort

OBJECTIVES

To describe the relations between baseline clinical characteristics of the Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) participants and their predicted probabilities for knee osteoarthritis (OA) structural (S) progression and/or pain (P) progression.

METHODS

Baseline clinical characteristics of the IMI-APPROACH participants were used for this study. Radiographs were evaluated according to Kellgren and Lawrence (K&L grade) and Knee Image Digital Analysis. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Numeric Rating Scale (NRS) were used to evaluate pain. Predicted progression scores for each individual were determined using machine learning models. Pearson correlation coefficients were used to evaluate correlations between scores for predicted progression and baseline characteristics. T-tests and χ² tests were used to evaluate differences between participants with high versus low progression scores.

RESULTS

Participants with high S progressions score were found to have statistically significantly less structural damage compared with participants with low S progression scores (minimum Joint Space Width, minJSW 3.56 mm vs 1.63 mm; p<0.001, K&L grade; p=0.028). Participants with high P progression scores had statistically significantly more pain compared with participants with low P progression scores (KOOS pain 51.71 vs 82.11; p<0.001, NRS pain 6.7 vs 2.4; p<0.001).

CONCLUSIONS

The baseline minJSW of the IMI-APPROACH participants contradicts the idea that the (predicted) course of knee OA follows a pattern of inertia, where patients who have progressed previously are more likely to display further progression. In contrast, for pain progressors the pattern of inertia seems valid, since participants with high P score already have more pain at baseline compared with participants with a low P score.

The instrumented sheep knee to elucidate insights into osteoarthritis development and progression: A sensitive and reproducible platform for integrated research efforts

BACKGROUND

Osteoarthritis of the knee is a very common condition that has been difficult to treat. The majority of cases are considered idiopathic. Much research effort remains focused on biology rather than the biomechanics of such joints. Some new methods were developed and validated to better appreciate the subtleties of the biomechanical integrity of joints, and how changes in biomechanics can contribute to osteoarthritis.

METHODS

Over the past 15 years our lab has enhanced the sensitivity of the assessment of knee biomechanics of an instrumented, trained large animal model (sheep) of osteoarthritis and integrated the findings with biological and histological assessments. These new methods include gait analysis before and after injury followed by robotic validation post-sacrifice, and more recently using Fibre Bragg Grating sensors to detect alterations in cartilage stresses.

RESULTS

A review of the findings obtained with this model are presented. The findings indicate that sheep, like humans, exhibit individual characteristics. They also indicate that joint kinetics, rather than kinematics may better define the alterations induced by injury. With the addition of Fibre Bragg Grating sensors, it has been possible to measure with good accuracy, alterations to cartilage stresses following a controlled knee injury.

INTERPRETATION

Using this model as Proof of Concept, this sheep system can now be viewed as a sensitive platform to address many questions related to risk for development of idiopathic osteoarthritis of the human knee, the efficacy of potential interventions to correct biomechanical disruptions, and how joint biomechanics and biology are integrated during aging.

Hoffa's fat pad thickness: a measurement method with sagittal MRI sequences

Background

Hoffa’s fat pad is a structure located within the fibrous joint capsule of the knee joint, but outside the synovial cavity. It plays an important biomechanical and metabolic role in knee joint, reducing the impact of forces generated by loading and producing cytokines. Changes in its size can induce modifications in the knee homeostasis. However, a great variability exists regarding its measurements. This work aims to evaluate the reliability of a measurement method of Hoffa’s fat pad dimensions through MRI.

Methods

3T sagittal IW 2D TSE fat-suppressed MRI sequences, taken from the OAI (Osteoarthritis initiative) database, of 191 male and female patients, aged between 40 and 80 years, were analysed; a manual measurement of the thickness of Hoffa’s fat pad of each subject was then performed by two different readers. The interobserver reliability and intraobserver reliability of the measurements were described by coefficient of variation (CV), Pearson correlation and Bland–Altman plots.

Results

All statistical analyses have shown that not significant intra- or interobservers differences were evident (intraobserver CV % for the first observer was 2.17% for the right knee and 2.24% for the left knee, while for the second observer 2.31% for the right knee and 2.24% for the left knee; linear correlation was for the first observer r = 0.96 for the right knee and r = 0.96 for the left knee, while for the second observer r = 0.97 for the right knee and r = 0.96 for the left knee; in addition, the interobserver CV % was 1.25% for the right knee and 1.21% for the left knee and a high interobserver linear correlation was found: r = 0.97 for the right knee and r = 0.96 for the left knee).

All results suggest that this manual measurement method of Hoffa’s fat pad thickness can be performed with satisfactory intra- and interobserver reliability.

Conclusions

Hoffa’s fat pad thickness can be measured, using sagittal MRI images, with this manual method that represents, for his high reliability, an effective means for the study of this anatomical structure.

Osteoarthritis year in review 2020: imaging

This narrative "Year in Review" highlights a selection of articles published between January 2019 and April 2020, to be presented at the OARSI World Congress 2020 within the field of osteoarthritis (OA) imaging. Articles were obtained from a PubMed search covering the above period, utilizing a variety of relevant search terms. We then selected original and review studies on OA-related imaging in humans, particularly those with direct clinical relevance, with a focus on the knee. Topics selected encompassed clinically relevant models of early OA, particularly imaging applications on cruciate ligament rupture, as these are of direct clinical interest and provide potential opportunity to evaluate preventive therapy. Further, imaging applications on structural modification of articular tissues in patients with established OA, by non-pharmacological, pharmacological and surgical interventions are summarized. Finally, novel deep learning approaches to imaging are reviewed, as these facilitate implementation and scaling of quantitative imaging application in clinical trials and clinical practice. Methodological or observational studies outside these key focus areas were not included. Studies focused on biology, biomechanics, biomarkers, genetics and epigenetics, and clinical studies that did not contain an imaging component are covered in other articles within the OARSI "Year in Review" series. In conclusion, exciting progress has been made in clinically validating human models of early OA, and the field of automated articular tissue segmentation. Most importantly though, it has been shown that structure modification of articular cartilage is possible, and future research should focus on the translation of these structural findings to clinical benefit.

Analyzing Femorotibial Cartilage Thickness Using Anatomically Standardized Maps: Reproducibility and Reference Data

Alterations in cartilage thickness (CTh) are a hallmark of knee osteoarthritis, which remain difficult to characterize at high resolution, even with modern magnetic resonance imaging (MRI), due to a paucity of standardization tools. This study aimed to assess a computational anatomy method producing standardized two-dimensional femorotibial CTh maps. The method was assessed with twenty knees, processed following three common experimental scenarios. Cartilage thickness maps were obtained for the femorotibial cartilages by reconstructing bone and cartilage mesh models in tree-dimension, calculating three-dimensional CTh maps, and anatomically standardizing the maps. The intra-operator accuracy (median (interquartile range, IQR) of -0.006 (0.045) mm), precision (0.152 (0.070) mm), entropy (7.02 (0.71) and agreement (0.975 (0.020))) results suggested that the method is adequate to capture the spatial variations in CTh and compare knees at varying osteoarthritis stages. The lower inter-operator precision (0.496 (0.132) mm) and agreement (0.808 (0.108)) indicate a possible loss of sensitivity to detect differences in a setting with multiple operators. The results confirmed the promising potential of anatomically standardized maps, with the lower inter-operator reproducibility stressing the need to coordinate operators. This study also provided essential reference data and indications for future research using CTh maps.

A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss - Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative

Objective

To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.

Methods

The OA Initiative Biomarker Consortium was a nested case–control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0–100 scale) after 2 years from baseline (n = 194), whereas non‐progressor knees did not have either of both (n = 200). Deep‐learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2‐year follow‐up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation.

Results

The mean ± SD MFTC cartilage loss in the progressor cohort was –181 ± 245 μm by manual segmentation (standardized response mean [SRM] –0.74), –144 ± 200 μm by the radiographic OA–based model (SRM –0.72), and –69 ± 231 μm by HRC‐based model segmentation (SRM –0.30). Cohen's d for rates of progression between progressor versus the non‐progressor cohort was –0.84 (P < 0.001) for manual, –0.68 (P < 0.001) for the automated radiographic OA model, and –0.14 (P = 0.18) for automated HRC model segmentation.

Conclusion

A fully automated deep‐learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.

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.

Simultaneous super-resolution and contrast synthesis of routine clinical magnetic resonance images of the knee for improving automatic segmentation of joint cartilage: data from the Osteoarthritis Initiative

PURPOSE

High resolution three-dimensional (3D) magnetic resonance (MR) images are well suited for automated cartilage segmentation in the human knee joint. However, volumetric scans such as 3D Double-Echo Steady-State (DESS) images are not routinely acquired in clinical practice which limits opportunities for reliable cartilage segmentation using (fully) automated algorithms. In this work, a method for generating synthetic 3D MR (syn3D-DESS) images with better contrast and higher spatial resolution from routine, low resolution, two-dimensional (2D) Turbo-Spin Echo (TSE) clinical knee scans is proposed.

METHODS

A UNet convolutional neural network is employed for synthesizing enhanced artificial MR images suitable for automated knee cartilage segmentation. Training of the model was performed on a large, publically available dataset from the OAI, consisting of 578 MR examinations of knee joints from 102 healthy individuals and patients with knee osteoarthritis.

RESULTS

The generated synthetic images have higher spatial resolution and better tissue contrast than the original 2D TSE, which allow high quality automated 3D segmentations of the cartilage. The proposed approach was evaluated on a separate set of MR images from 88 subjects with manual cartilage segmentations. It provided a significant improvement in automated segmentation of knee cartilages when using the syn3D-DESS images compared to the original 2D TSE images.

CONCLUSION

The proposed method can successfully synthesize 3D DESS images from 2D TSE images to provide images suitable for automated cartilage segmentation.

Predicting knee replacement in participants eligible for disease-modifying osteoarthritis drug treatment with structural endpoints

OBJECTIVE

Evaluate associations between 2-year change in radiographic or quantitative magnetic resonance imaging (qMRI) structural measures, and knee replacement (KR), within a subsequent 7-year follow-up period.

METHOD

Participants from the Osteoarthritis Initiative were selected based on potential eligibility criteria for a disease-modifying osteoarthritis (OA) drug trial: Kellgren-Lawrence grade 2 or 3; medial minimum joint space width (mJSW) ≥2.5 mm; knee pain at worst 4-9 in the past 30 days on an 11-point scale, or 0-3 if medication was taken for joint pain; and availability of structural measures over 2 years. Mean 2-year change in structural measures was estimated and compared with two-sample independent t-tests for KR and no KR. Area under the receiver operating characteristic curve (AUC) was estimated using 2-year change in structural measures for prediction of future KR outcomes.

RESULTS

Among 627 participants, 107 knees underwent KR during a median follow-up of 6.7 years after the 2-year imaging period. Knees that received KR during follow-up had a greater mean loss of cartilage thickness in the total femorotibial joint and medial femorotibial compartment on qMRI, as well as decline in medial fixed joint space width on radiographs, compared with knees that did not receive KR. These imaging measures had similar, although modest discrimination for future KR (AUC 0.62, 0.60, and 0.61, respectively).

CONCLUSIONS

2-year changes in qMRI femorotibial cartilage thickness and radiographic JSW measures had similar ability to discriminate future KR in participants with knee OA, suggesting that these measures are comparable biomarkers/surrogate endpoints of structural progression.

Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images

OBJECTIVE

To develop and validate a 3D registration approach by which double echo steady state (DESS) MR images with cartilage thickness segmentations are used to extract the cartilage transverse relaxation time (T2) from multi-echo-spin-echo (MESE) MR images, without direct segmentations for MESE.

MATERIALS AND METHODS

Manual DESS segmentations of 89 healthy reference knees (healthy) and 60 knees with early radiographic osteoarthritis (early ROA) from the Osteoarthritis Initiative were registered to corresponding MESE images that had independent direct T2 segmentations. For validation purposes, (a) regression analysis of deep and superficial cartilage T2 was performed and (b) between-group differences between healthy vs. early ROA knees were compared for registered vs. direct MESE analysis.

RESULTS

Moderate to high correlations were observed for the deep (r = 0.80) and the superficial T2 (r = 0.81), with statistically significant between-group differences (ROA vs. healthy) of + 1.4 ms (p = 0.002) vs. + 1.3 ms (p < 0.001) for registered vs. direct T2 segmentation in the deep, and + 1.3 ms (p = 0.002) vs. + 2.3 ms (p < 0.001) in the superficial layer.

DISCUSSION

This registration approach enables extracting cartilage T2 from MESE scans using DESS (cartilage thickness) segmentations, avoiding the need for direct MESE T2 segmentations.

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.

Cartilage survival of the knee strongly depends on malalignment: a survival analysis from the Osteoarthritis Initiative (OAI)

PURPOSE

Progression of osteoarthritis over time is poorly understood. The aim of the current study was to establish a timeline of "cartilage survival rate" per subregion of the knee in relation to mechanical alignment of the lower extremity. The study hypothesized that there are differences in progression of osteoarthritis between varus, valgus and physiologic lower extremity alignment.

METHODS

Based on hip-knee-ankle standing radiographs at baseline, 234 knees had physiologic (180° ± 3°, mean 179.7°), 158 knees had varus (< 177°; mean 174.5°) and 66 knees valgus (> 183°; mean 185.2°) alignment (consecutive knees of the OAI "Index Knee" group, n = 458; mean age 61.7; 264 females). The Osteoarthritis Initiative (OAI; a multi-center, longitudinal, prospective observational study of knee osteoarthritis [30] using MRIs) defines progressive OA as a mean decrease of cartilage thickness of 136 µm/year and a mean decrease of cartilage volume by 5% over 1 year (DESS sequences, MRI). A Kaplan-Meier curve was generated for osteoarthritis progression based on OAI criteria.

RESULTS

Osteoarthritis progression based on volume decrease of 5% in varus knees occurred after 30.8 months (medial femoral condyle), after 37 months (medial tibia), after 42.9 months (lateral femoral condyle) and 43.4 months (lateral tibia), respectively. In a valgus alignment progression was detectable after 31.5 months (lateral tibia), after 36.2 months (lateral femoral condyle), after 40.4 months (medial femoral condyle) and 43.8 months (medial tibia), respectively. The physiological alignment shows a progression after 37.8 months (medial femoral condyle), after 41.6 months (lateral tibia), after 41.7 months (medial tibia) and after 43 months (lateral femoral condyle), respectively.

CONCLUSION

Based on data from the OAI, the rate and location (subregion) of osteoarthritis progression of the knee is strongly associated with lower extremity mechanical alignment.

LEVEL OF EVIDENCE

Level I (prognostic study).

Changes in Medial Meniscal Three-Dimensional Position and Morphology As Predictors of Knee Replacement in Rapidly Progressing Knee Osteoarthritis: Data From the Osteoarthritis Initiative

OBJECTIVE

To assess whether quantitative changes in the meniscus predict progression from early knee osteoarthritis (OA) to knee replacement (KR).

METHODS

A nested case-control study was conducted among Osteoarthritis Initiative participants: all 35 case knees with baseline Kellgren/Lawrence (K/L) grade ≤2 that had KR between 36 and 60 months were matched 1:1 by age, sex, and baseline K/L grade to 35 control knees without subsequent KR. Quantitative 3-dimensional medial meniscus position and morphologic measures were determined from magnetic resonance imaging at the visit just before KR and 2 years before. Paired t-tests and case-control odds ratios (ORs, standardized per SD of change in controls) were used to compare changes between groups.

RESULTS

Cases (52% women, age 65 ± 7 years, body mass index [BMI] 30 ± 4 kg/m² , K/L grades 0/1/2: 5/8/22 participants, respectively) and controls (52% women, age 64 ± 7 years, BMI 30 ± 5 kg/m² , K/L grades 0/1/2: 9/4/22 participants, respectively) were similar. Compared to control knees, KR case knees displayed longitudinal changes, specifically, a decrease in tibial plateau coverage, an increase in meniscal extrusion, and a decrease in meniscal width. The odds for KR increased with greater reduction in the percentage of tibial plateau coverage (OR 2.28 [95% CI confidence interval (95% CI) 1.43, 3.64]), a greater increase in maximal extrusion (OR 1.40 [95% CI 1.12, 1.75]), and a greater reduction of mean meniscal width (OR 2.01 [95% CI 1.23, 3.26]). The odds for KR increased with medial compartment cartilage thickness loss (OR 2.86 [95% CI 1.51, 5.41]) for comparison.

CONCLUSION

Quantitative measures of meniscal position and morphology are associated with subsequent KR in knees with rapidly progressing knee OA. These findings show that structural changes of the meniscus are related to an important clinical and economic outcome of knee OA.

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers

BACKGROUND

Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.

PURPOSE

To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring.

STUDY TYPE

Retrospective.

POPULATION

In all, 176 MRI studies of subjects at varying stages of osteoarthritis.

FIELD STRENGTH/SEQUENCE

Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T.

ASSESSMENT

A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans.

STATISTICAL TESTS

Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference.

RESULTS

DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22).

DATA CONCLUSION

Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.

Dietary Patterns and Progression of Knee Osteoarthritis: Data from the Osteoarthritis Initiative

BACKGROUND

While some individual foods and nutrients have been associated with knee osteoarthritis (KOA) progression, the association between dietary patterns and KOA progression has received little research attention.

OBJECTIVE

The objective of this study was to determine whether dietary patterns, derived by principal components analysis (PCA), are associated with KOA progression.

METHODS

In the Osteoarthritis Initiative (OAI), a prospective cohort with clinical centers in Maryland, Ohio, Pennsylvania, and Rhode Island, 2757 participants with existing KOA (mean age 62 y) and diet assessed at baseline were followed for ≤72 mo. Using PCA, Western and prudent dietary patterns were derived. Radiographic KOA progression was assessed using 2 separate measures, 1 full Kellgren-Lawrence (KL) grade increase and loss in joint space width (JSW). Symptomatic KOA progression was defined as an increase in or remaining in 1 of the 2 highest classification categories of the Western Ontario and McMaster Universities Arthritis Index (WOMAC).

RESULTS

Adherence to Western and prudent dietary patterns was significantly associated with radiographic and symptomatic progression of KOA. With increasing Western pattern score, there was increased KL-worsening risk (compared with quartile 1, HR for quartile 4: 1.30; 95% CI: 1.05, 1.61; P-trend < 0.01) and increased odds of progression to higher WOMAC score (compared with quartile 1, OR for quartile 4: 1.39; 95% CI: 1.18, 1.63; P-trend < 0.01) but no significant change in JSW loss. With increasing prudent pattern score there was decreased KL-worsening risk (compared with quartile 1, HR for quartile 4: 0.79; 95% CI: 0.64, 0.98; P-trend = 0.02), decreased JSW loss (quartile 1: 0.46 mm; quartile 4: 0.38 mm; P-trend < 0.01), and decreased odds of higher WOMAC progression (compared with quartile 1, OR for quartile 4 0.73; 95% CI: 0.62, 0.86; P-trend < 0.01) in multivariable adjusted models.

CONCLUSIONS

Adherence to a Western dietary pattern was associated with increased radiographic and symptomatic KOA progression, while following a prudent pattern was associated with reduced progression. In general, for people already diagnosed with KOA, eating a diet rich in fruits, vegetables, fish, whole grains, and legumes may be related to decreased radiographic and symptomatic disease progression.

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

OBJECTIVES

In the phase II FGF-18 Osteoarthritis Randomized Trial with Administration of Repeated Doses (FORWARD) study, sprifermin demonstrated cartilage modification in the total femorotibial joint and in both femorotibial compartments by MRI in patients with knee osteoarthritis. Here, we evaluate whether sprifermin reduces cartilage loss and increases cartilage thickness, independent of location.

METHODS

Patients were randomised 1:1:1:1:1 to three once-weekly intra-articular injections of 30 µg sprifermin every 6 months (q6mo); 30 µg sprifermin every 12 months (q12mo); 100 µg sprifermin q6mo; 100 µg sprifermin q12mo; or placebo. Post-hoc analysis using thinning/thickening scores and ordered values evaluated femorotibial cartilage thickness change from baseline to 24 months independent of location. Changes were indirectly compared with those of Osteoarthritis Initiative healthy subjects.

RESULTS

Thinning scores were significantly lower for sprifermin 100 µg q6mo versus placebo (mean (95% CI) difference: 334 µm (114 to 554)), with a cartilage thinning score similar to healthy subjects. Thickening scores were significantly greater for sprifermin 100 µg q6mo, 100 µg q12mo and 30 µg q6mo versus placebo (mean (95% CI) difference: 425 µm (267 to 584); 450 µm (305 to 594) and 139 µm (19 to 259), respectively) and more than doubled versus healthy subjects.

CONCLUSIONS

Sprifermin increases cartilage thickness, and substantially reduces cartilage loss, expanding FORWARD primary results.

TRIAL REGISTRATION NUMBER

NCT01919164.

Understanding Atherosclerosis Through an Osteoarthritis Data Set

Atherosclerotic cardiovascular disease remains a worldwide epidemic and one of the leading causes of death nowadays. Vessel wall imaging can be used to understand the development and progression of atherosclerosis, but it is rarely done because of the high cost. We recently identified the Osteoarthritis Initiative, a large prospective cohort study of knee osteoarthritis, which might serve as a valuable source for atherosclerosis research with its serial knee magnetic resonance imaging data. We have found that these images are suitable for vessel wall image analysis of the lower extremity arteries. Here, we will introduce the Osteoarthritis Initiative data set and explain why it could be used for cardiovascular research purposes. Also, we will briefly comment on peripheral artery atherosclerosis as it is covered in the Osteoarthritis Initiative image data set and review the use of vessel wall imaging for studying atherosclerosis. We think data mining of imaging studies, not originally designed on cardiovascular research, can not only maximize the value of the imaging data set but also boost our understanding of atherosclerosis.

Variability, agreement and reliability of MRI knee landmarks

Surface mesh reconstructions of bones are often required to define landmark-based coordinate systems, regions of interest and morphological features when studying the soft tissues of the knee from MRI scans. This study reports the variability, agreement and reliability of osseous landmarks to better understand their downstream effects. Fifteen landmarks were defined on the distal femur and twelve on the proximal tibia. Surface meshes were created from twenty right knee MRI scans with a mean subject age of 30.9 years. A single observer identified landmarks on all twenty knees, while three observers repeated the observations three times on a subset of eight knees. All observations were aligned to the Procrustes mean shapes. Principal component analysis was used to study inter-subject variability and two-way ANOVA for inter- and intra-observer agreement and reliability. Inter-subject landmark variation ranged from 0.6 to 5.26 mm, while inter- and intra-observer agreement were at most 5.1 and 5.69 mm respectively. Between-observer reliability ranged from 0.07 to 0.98 while within-observer values were between 0.51 and 0.98. Landmarks derived from fitted spheres or circles often performed well, while most others had their poorest agreement or greatest variation limited to only one or two cardinal directions.

Deep convolutional neural network for segmentation of knee joint anatomy

PURPOSE

To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation.

METHODS

A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data.

RESULTS

The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm.

CONCLUSION

The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

Three-dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

One of the most important characteristic of knee osteoarthritis (OA) is the joint space (JS) width narrowing. Measurements are usually performed on two dimensional (2D) X-rays. We propose and validate a new method to assess the 3D joint space at the medial knee compartment using high resolution peripheral computed tomography images. A semi-automated method was developed to obtain a distance 3D map between femur an tibia with the following parameters: volume, minimum, maximum, mean, standard deviation, median, asymmetry, and entropy. We analyzed 71 knee specimens (mean age: 85 years), radiographs were performed for the Kellgren Lawrence (KL) score grading. In a subgroup of 41 specimens, the histopathological Outerbridge and meniscal classifications were performed and then cores were harvested from the tibial plateau in three different positions (posterior, central, and peripheral) and imaged at 10 µm of resolution to measure the cartilage thickness. Minimum, maximum, mean, and median were statistically lower and entropy higher between knee specimens classified as KL = 0 and KL = 3-4. Gr1 and 2 were statistically different from Gr3-4 for minimum, asymmetry, entropy using the Outerbridge classification and Gr1 was statistically different from Gr3-4 using the meniscal classification. Asymmetry, minimum, mean, median and entropy were significantly correlated with cartilage thickness. Parameters extracted from a 3D map of the medial joint space indicate local variations of JS and are related to local measurements of tibial cartilage thickness, and could be consequently useful to identify early OA. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:2380-2391, 2018.

Responsiveness of Infrapatellar Fat Pad Volume Change to Body Weight Loss or Gain: Data from the Osteoarthritis Initiative

Obesity is a potent risk factor for knee osteoarthritis (OA) that is driven by mechanical and potentially endocrine mechanisms, and it affects women more frequently than men. The infrapatellar fat pat (IPFP) represents a potential link between obesity, intra-articular inflammation and structural pathology. Here we investigate whether the IPFP is responsive to body weight loss/gain in women and how its responsiveness to weight change compares to that of subcutaneous fat (SCF) of the thigh. All female participants of the Osteoarthritis Initiative (OAI) with ≥10% weight loss/gain between baseline and a 2-year follow-up were included. Within-subject changes in IPFP volume and SCF cross-sectional areas (CSA) were determined from 3-T magnetic resonance imaging. Linear regression was used to assess the association between change in weight, IPFP volume, and SCF CSA. In the 38 participants with ≥10% weight loss over 2 years (age 59.3 ± 9.1 years, mean loss = 15.9%), there was a significant reduction in IPFP volume (-2.2%, p = 0.02) as well as in SCF CSA (-22%, p < 0.001). In the 34 participants with ≥10% gain (age 61.5 ± 8.7 years, mean gain = 15.9%), there was a significant increase in SCF CSA (+26%, p < 0.001) but not in IPFP volume (0.2%, p = 0.87). Weight change was significantly associated with SCF CSA change (r = 0.76, p < 0.001) but not with IPFP volume change (r = 0.11, p = 0.37). In this first longitudinal, observational study investigating the responsiveness of IPFP and SCF to weight change, IPFP morphology was found responsive to weight loss but not to weight gain. Overall, the responsiveness of the IPFP was substantially less than that of the SCF.

Simultaneous bilateral-knee MR imaging

PURPOSE

To demonstrate and evaluate the scan time and quantitative accuracy of simultaneous bilateral-knee imaging compared with single-knee acquisitions.

METHODS

Hardware modifications and safety testing was performed to enable MR imaging with two 16-channel flexible coil arrays. Noise covariance and sensitivity-encoding g-factor maps for the dual-coil-array configuration were computed to evaluate coil cross-talk and noise amplification. Ten healthy volunteers were imaged on a 3T MRI scanner with both dual-coil-array bilateral-knee and single-coil-array single-knee configurations. Two experienced musculoskeletal radiologists compared the relative image quality between blinded image pairs acquired with each configuration. Differences in T₂ relaxation time measurements between dual-coil-array and single-coil-array acquisitions were compared with the standard repeatability of single-coil-array measurements using a Bland-Altman analysis.

RESULTS

The mean g-factors for the dual-coil-array configuration were low for accelerations up to 6 in the right-left direction, and minimal cross-talk was observed between the two coil arrays. Image quality ratings of various joint tissues showed no difference in 89% (95% confidence interval: 85-93%) of rated image pairs, with only small differences ("slightly better" or "slightly worse") in image quality observed. The T₂ relaxation time measurements between the dual-coil-array configuration and the single-coil configuration showed similar limits of agreement and concordance correlation coefficients (limits of agreement: -0.93 to 1.99 ms; CCC: 0.97 (95% confidence interval: 0.96-0.98)), to the repeatability of single-coil-array measurements (limits of agreement: -2.07 to 1.96 ms; CCC: 0.97 (95% confidence interval: 0.95-0.98)).

CONCLUSION

A bilateral coil-array setup can image both knees simultaneously in similar scan times as conventional unilateral knee scans, with comparable image quality and quantitative accuracy. This has the potential to improve the value of MRI knee evaluations. Magn Reson Med 80:529-537, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Threshold Limit Graphical Approach to Understanding Outcome Predictive Metrics: Data from the Osteoarthritis Initiative

Scatter plots, bar charts, linear regressions, analysis of variance, and other graphics and tests are frequently used to document associations between an independent variable and an outcome. However, these methods are also frequently limited when understanding how to use an independent variable in subsequent research or patient management. A novel graphical approach to visualizing data—the threshold limit graph—was therefore developed.

Publically available data from the Osteoarthritis Initiative was used to illustrate the graphical approach to understanding the association between the change in joint space width (ΔJSW, independent variable) over four years, and knee symptoms at four years (using the Knee Injury and Osteoarthritis Outcome Score [KOOS], dependent variable).

Using data for 4,202 knees, the traditional scatter plot and linear regression approach showed a significant but weak linear relationship between the symptom subscore of the KOOS and ΔJSW. However, the threshold level of ΔJSW that affects symptoms was not clear from the data. The same dataset was then plotted using the threshold limit graphical approach, which revealed a non-linear relationship between the variables. In contrast to the scatter plot, plotting the average KOOS symptom subscore for subgroups of the data, with each subgroup defined using sequentially increasing or decreasing ΔJSW thresholds revealed that symptoms got worse with joint space loss, but only when there was a significant amount of ΔJSW. A threshold limit analysis was repeated using small, randomly selected subsets of the data (N = ~100) to demonstrate the utility of the technique for identifying trends in smaller datasets.

The threshold limit graph is a simple, graphical approach that may prove helpful in understanding how an independent variable might be used to predict outcomes. This approach provides an additional option for visualizing and quantifying associations between variables.

Etanercept for treating axial spondyloarthritis

INTRODUCTION

Axial spondyloarthritis is an inflammatory rheumatic disease causing back pain, functional impairment and potential ankylosis in the advanced stage. In this context, TNF blockers have been a major therapeutic advance. Etanercept is a soluble recombinant TNF receptor fusion protein in this vain. Areas covered: The aim of this review is to summarize the current published data concerning the efficacy and tolerance of etanercept in axial spondyloarthrits. The authors performed a systematic review on PubMed, using 'etanercept' and 'spondyloarthritis', 'axial spondyloarthritis' or 'ankylosing spondylitis' keywords. Expert opinion: Etanercept showed clinical efficacy on the axial (non-radiographic and radiographic) and peripheral manifestations (peripheral arthritis and enthesitis) of axial spondyloarthritis (Ax-SpA). Among the extra-articular manifestations, it works on psoriasis but not on inflammatory bowel disease, with a lack of efficacy data in anterior uveitis. Etanercept also demonstrated an interesting tolerance profile and good drug survival rates after 5 years. Etanercept was also shown to reduce MRI inflammation on the spine and the sacroiliac joints. However, like other TNF blockers, its impact on radiographic progression could not be fully demonstrated. In the context of upcoming new biologic targeted treatments, head-to-head and longer-term randomized controlled trials are now required to further define the role of etanercept in spondyloarthritis treatment strategies.

Five-Year Follow-up of Knee Joint Distraction: Clinical Benefit and Cartilaginous Tissue Repair in an Open Uncontrolled Prospective Study

Objective In end-stage knee osteoarthritis, total knee arthroplasty (TKA) may finally become inevitable. At a relatively young age, this comes with the risk of future revision surgery. Therefore, in these cases, joint preserving surgery such as knee joint distraction (KJD) is preferred. Here we present 5-year follow-up data of KJD. Design Patients ( n = 20; age <60 years) with conservative therapy resistant tibiofemoral osteoarthritis considered for TKA were treated. Clinical evaluation was performed by questionnaires. Change in cartilage thickness was quantified on radiographs and magnetic resonance images (MRI). The 5-year changes after KJD were evaluated and compared with the natural progression of osteoarthritis using Osteoarthritis Initiative data. Results Five-years posttreatment, patients still reported clinical improvement from baseline: ΔWOMAC (Western Ontario and McMaster Universities Arthritis Index) +21.1 points (95% CI +8.9 to +33.3; P = 0.002), ΔVAS (visual analogue scale score) pain -27.6 mm (95%CI -13.3 to -42.0; P < 0.001), and minimum radiographic joint space width (JSW) of the most affected compartment (MAC) remained increased as well: Δ +0.43 mm (95% CI +0.02 to +0.84; P = 0.040). Improvement of mean JSW (x-ray) and mean cartilage thickness (MRI) of the MAC, were not statistically different from baseline anymore (Δ +0.26 mm; P = 0.370, and Δ +0.23 mm; P = 0.177). Multivariable linear regression analysis indicated that KJD treatment was associated with significantly less progression in mean and min JSW (x-ray) and mean cartilage thickness (MRI) compared with natural progression (all Ps <0.001). Conclusions KJD treatment results in prolonged clinical benefit, potentially explained by an initial boost of cartilaginous tissue repair that provides a long-term tissue structure benefit as compared to natural progression. Level of evidence, II.

Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas

Objective

To validate a semi-automated method for thigh muscle and adipose tissue cross-sectional area (CSA) segmentation from MRI.

Materials and methods

An active shape model (ASM) was trained using 113 MRI CSAs from the Osteoarthritis Initiative (OAI) and combined with an active contour model and thresholding-based post-processing steps. This method was applied to 20 other MRIs from the OAI and to baseline and follow-up MRIs from a 12-week lower-limb strengthening or endurance training intervention (n = 35 females). The agreement of semi-automated vs. previous manual segmentation was assessed using the Dice similarity coefficient and Bland-Altman analyses. Longitudinal changes observed in the training intervention were compared between semi-automated and manual segmentations.

Results

High agreement was observed between manual and semi-automated segmentations for subcutaneous fat, quadriceps and hamstring CSAs. With strength training, both the semi-automated and manual segmentation method detected a significant reduction in adipose tissue CSA and a significant gain in quadriceps, hamstring and adductor CSAs. With endurance training, a significant reduction in adipose tissue CSAs was observed with both methods.

Conclusion

The semi-automated approach showed high agreement with manual segmentation of thigh muscle and adipose tissue CSAs and showed longitudinal training effects similar to that observed using manual segmentation.

Electronic supplementary material

The online version of this article (doi:10.1007/s10334-017-0622-3) contains supplementary material, which is available to authorized users.

Medial femur T2 Z-scores predict the probability of knee structural worsening over 4-8 years: Data from the osteoarthritis initiative

OBJECTIVE

The purpose of this study was to determine the probability of structural worsening of knee cartilage and whole joint degeneration over 4-8 years based on cartilage T₂ Z-scores at baseline.

DESIGN

Right knees with Kellgren-Lawrence (KL) grades of 0-2 in 587 participants from the Osteoarthritis Initiative were studied. 3T MR images were used to perform baseline cartilage T₂ quantification and assess 4-year changes in cartilage morphology (WORMS scoring) in 5 regions. Changes in joint space narrowing (JSN) and KL were assessed over 8 years. T₂ Z-scores were based on a reference database of knees without morphologic cartilage degeneration at baseline. Odds ratios for, and predicted probabilities of any worsening in WORMS cartilage, JSN and KL grade were obtained from logistic regression models.

RESULTS

A one-unit increase in the baseline medial femur T₂ Z-score was associated with cartilage worsening in the same region (OR = 1.59; P < 0.0001) and in any region (OR = 1.37; P < 0.0001), and with worsening JSN (OR = 1.82; P < 0.0001) and KL grades (OR = 1.69; P < 0.0001). Predicted probabilities of worsening in knees with a medial femur T₂ Z-score from 2-4 were 38% for medial femur cartilage WORMS, 70% for any cartilage region, 28% for increasing JSN and 31% for increasing KL grade.

CONCLUSION

Knees with elevated cartilage T₂ (especially in the medial femur and those that are 2 to 4 SDs above the mean reference values) are significantly more likely to have structural worsening over 4 to 8 years. Knowing cartilage T₂ Z-scores may aid in targeting prevention efforts at early stages of osteoarthritis.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1128-1136.

Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative: Tibial bone texture & knee OA progression

OBJECTIVES

To examine whether trabecular bone texture (TBT) parameters assessed on computed radiographs could predict knee osteoarthritis (OA) progression.

METHODS

This study was performed using data from the Osteoarthritis Initiative (OAI). 1647 knees in 1124 patients had bilateral fixed flexion radiographs acquired 48 months apart. Images were semi-automatically segmented to extract a patchwork of regions of interest (ROI). A fractal texture analysis was performed using different methods. OA progression was defined as an increase in the joint space narrowing (JSN) over 48 months. The predictive ability of TBT was evaluated using logistic regression and receiver operating characteristic (ROC) curve. An optimization method for features selection was used to reduce the size of models and assess the impact of each ROI.

RESULTS

Fractal dimensions (FD's) were predictive of the JSN progression for each method tested with an area under the ROC curve (AUC) up to 0.71. Baseline JSN grade was not correlated with TBT parameters (R < 0.21) but had the same predictive capacity (AUC 0.71). The most predictive model included the clinical covariates (age, gender, body mass index (BMI)), JSN and TBT parameters (AUC 0.77). From a statistical point of view we found higher differences in TBT parameters computed in medial ROI between progressors and non-progressors. However, the integration of TBT results from the whole patchwork including the lateral ROIs in the model provided the best predictive model.

CONCLUSIONS

Our findings indicate that TBT parameters assessed in different locations in the joint provided a good predictive ability to detect knee OA progression.

Role of physical activity in cartilage damage progression of subjects with baseline full-thickness cartilage defects in medial tibiofemoral compartment: data from the Osteoarthritis Initiative

OBJECTIVE

To assess the association between physical activity and cartilage damage progression in medial tibiofemoral compartment (MTFC) using 2-year follow-up magnetic resonance imaging (MRI) in subjects with denuded areas of subchondral bone (dABs) at the central weight-bearing medial femur (cMF) at baseline MRI examination.

METHODS

One hundred subjects from the Osteoarthritis Initiative (OAI) progression cohort with dABs at the cMF at 3T MRI at baseline (51% men; mean age 62.2 years, range 45-79) were included. Sagittal 3D dual-echo steady-state with water excitation images were used to assess 2-year MTFC cartilage change. Associations between 2-year average Physical Activity Scale for the Elderly (PASE) and 2-year MTFC cartilage change were assessed by linear regression analysis. Subgroup analyses were performed.

RESULTS

No associations between PASE and 2-year MTFC cartilage change were observed in the entire cohort. Similarly, in the subgroup with cartilage loss during the 2 years, the non-refuted confidence intervals for the regression coefficients were tightly clustered around the null value (regression coefficients for: mean cMF.ThCtAB = -0.00059; 98.75% CI: -0.00130 to 0.00012), cMF.dAB% = 0.02176; 98.75% CI: -0.02514 to 0.06865, Mean MT.ThCtAB = -0.00013; 98.75% CI: -0.00064 to 0.00038, MT.dAB% = 0.02543; 98.75% CI: -0.01485 to 0.06571.

CONCLUSION

In the entire group of subjects with dABs at the cMF at baseline, no association between physical activity and 2-year MTFC cartilage change was detected. Due to the limited sample size of our study, small-sized effects may not have been detected in our study.

Targeted therapies: what they teach us about the pathogenesis of psoriasis and psoriatic arthritis

INTRODUCTION

Biologic therapy has revolutionized treatment pathways in psoriatic joint and skin disease. It has also provided a useful tool with which pathological pathways of this condition may be explored. Areas covered: This review presents data on the clinical and biological effects of targeted therapy in psoriatic arthritis and psoriasis. Therapeutic agents covered include inhibitors of TNFα, inhibitors of the IL-23/IL-17 axis and inhibitors of intracellular small molecules involved in the transduction of the inflammatory signal. Trial data on clinical and imaging efficacy is reviewed in parallel with studies on biological effects at tissue level. Pathological insights gained from the use of these treatments are explored. Expert commentary: A close relationship exists between specific pathological types and clinical manifestations of psoriatic disease, including responses to treatment. Studying these relationships is likely to improve understanding of disease and enable rational selection of specific treatments for patients with specific pathotypes.