The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee

Radiomic features of the medial meniscus predicts incident destabilizing meniscal tears: Data from the osteoarthritis initiative

The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit using intermediate-weighted fat-suppressed MR images. One reader manually segmented each participant's anterior and posterior horn of the medial menisci at the OAI baseline visit. Next, 61 different radiomic features were extracted from each medial meniscus horn. We performed a classification and regression tree (CART) analysis to determine the classification rules and important variables that predict incident destabilizing meniscal tear. The CART correctly classified 24 of the 34 cases and 172 out of 181 controls with a sensitivity of 70.6% and a specificity of 95.0%. The CART identified large zone high gray level emphasis (i.e., more coarse texture) from the posterior horn as the most important variable to classify who would develop an incident destabilizing medial meniscal tear. The use of radiomic features provides sensitive and quantitative measures of meniscal alterations, allowing us to intervene and prevent destabilizing meniscal tears.

3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology

OBJECTIVES

This study aimed to assess the diagnostic image quality and compare the knee cartilage segmentation results using a controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-accelerated 3D-dual echo steady-state (DESS) research package sequence in the knee.

MATERIALS AND METHODS

A total of 64 subjects underwent both two- and fourfold CAIPIRINHA-accelerated 3D-DESS and DESS without parallel acceleration technique of the knee on a 3.0 T system. Two musculoskeletal radiologists evaluated the images independently for image quality and diagnostic capability following randomization and anonymization. The consistency of automatic segmentation results between sequences was explored using an automatic knee cartilage segmentation research application. The descriptive statistics and inter-observer and inter-method concordance of various acceleration sequences were investigated. P values < .05 were considered significant.

RESULTS

For image quality evaluation, the image signal-to-noise ratio and contrast-to-noise ratio decreased with the decrease in scanning time. However, it is accompanied by the reduction of artifacts. Using 3D-DESS without parallel acceleration technique as the standard for cartilage grading diagnosisand the diagnostic agreement of two- and fourfold CAIPIRINHA-accelerated 3D-DESS was good, kappa value was 0.860 (P < .001) and 0.804 (p < 0.001), respectively. Regarding cartilage defects, the sensitivity and specificity of the twofold acceleration 3D-CAIPIRINHA-DESS were 95.56% and 97.70%, and the fourfold CAIPIRINHA-accelerated 3D-DESS were 91.49% and 97.65%, respectively. The intraclass correlation coefficients of various sequences in cartilage segmentation were almost all greater than 0.9.

CONCLUSION

The CAIPIRINHA-accelerated 3D-DESS sequence maintained comparable diagnostic and segmentations performance of knee cartilage after a 60% scan time reduction.

Infrapatellar fat pad size and subcutaneous fat in knee osteoarthritis radiographic progression: data from the osteoarthritis initiative

OBJECTIVES

Adipose tissue has been associated with knee osteoarthritis (KOA) pathogenesis, but the longitudinal changes in adipose tissue with KOA progression have not been carefully evaluated. This study aimed to determine if longitudinal changes of systemic and local adipose tissue is associated with radiographic progression of KOA.

METHODS

This case-control study used data from the Osteoarthritis Initiative (OAI) and included 315 cases (all the right knees with a minimum of Kellgren-Lawrence score (KL) of 0 and an increase of ≥ 1 KL from baseline to 48 months) and 315 controls matched by age, sex, race, and baseline KL. Cross sectional area of IPFP (IPFP CSA) and subcutaneous adipose tissue around the distal thigh (SCATthigh) were measured using MRI images at baseline and 24 months. Conditional logistic regression models were fitted to estimate associations of obesity markers, IPFP CSA, and SCATthigh with radiographic KOA progression. Mediation analysis was used to assess whether IPFP CSA or SCATthigh mediates the relationships between baseline BMI and radiographic KOA progression.

RESULTS

24-month changes of IPFP CSA (ΔIPFP CSA) and SCATthigh (ΔSCATthigh) were significantly greater in cases compared to controls, whereas Δ BMI and Δ abdominal circumference were similar in both groups during follow-up. Adjusted ORs for radiographic KOA progression were 9.299, 95% CI (5.357-16.141) per 1 SD increase of Δ IPFP CSA and 1.646, 95% CI (1.288-2.103) per 1 SD increase of Δ SCATthigh. ΔIPFP CSA mediated the association between baseline BMI and radiographic KOA progression (87%).

CONCLUSIONS

Subjects with radiographic progression of KOA, had significant increases in IPFP CSA and subcutaneous adipose tissue while BMI and abdominal circumference remained stable. Additional studies are needed to confirm these associations.

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Clinical validation of fully automated laminar knee cartilage transverse relaxation time (T2) analysis in anterior cruciate ligament (ACL)-injured knees- on behalf of the osteoarthritis (OA)-Bio consortium

Background

Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees.

Methods

We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner.

Results

Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method.

Conclusions

This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).

Interactions of cumulative load with biomarkers of cartilage turnover predict knee cartilage change over 2 years: data from the osteoarthritis initiative

The purpose was to investigate relationships of cumulative load and cartilage turnover biomarkers with 2-year changes in cartilage in knee osteoarthritis. From participants with Kellgren-Lawrence (KL) grades of 1 to 3, cartilage thickness and transverse relaxation time (T2) were computed from 24-month (baseline) and 48-month magnetic resonance images. Cumulative load was the interaction term of the Physical Activity Scale for the Elderly (PASE) and body mass index (BMI). Serum cartilage oligomeric matrix protein (COMP) and the nitrated form of type II collagen (Coll2-1 NO2) were collected at baseline. Multiple regressions (adjusted for baseline age, KL grade, cartilage measures, pain, comorbidity) evaluated the relationships of cumulative load and biomarkers with 2-year changes. In 406 participants (63.7 (8.7) years), interactions of biomarkers with cumulative load weakly predicted 2-year cartilage changes: (i) COMP × cumulative load explained medial tibia thickness change (R2 increased 0.062 to 0.087, p < 0.001); (ii) Coll2-1 NO2 × cumulative load explained central medial femoral T2 change (R2 increased 0.177 to 0.210, p < 0.001); and (iii) Coll2-1 NO2 × cumulative load explained lateral tibia T2 change (R2 increased 0.166 to 0.188, p < 0.001). Moderate COMP or Coll2-1 NO2 at baseline appeared protective. High COMP or Coll2-1 NO2, particularly with high BMI and low PASE, associated with worsening cartilage. Moderate serum concentrations of cartilage turnover biomarkers, at high and low physical activity, associated with maintained cartilage outcomes over 2 years. In conclusion, high concentrations of cartilage turnover biomarkers, particularly with high BMI and low physical activity, associated with knee cartilage thinning and increasing T2 over 2 years. Key Points • Higher quality cartilage may be better able to tolerate a larger cumulative load than poor quality cartilage. • Among participants enrolled in the Osteoarthritis Initiative Biomarkers Consortium Project, a representation of cumulative load exposure and its interaction with cartilage turnover biomarkers were weakly related with 2-year change in knee cartilage. • These findings suggest that cartilage turnover is a factor that modifies the relationship between loading exposure and cartilage loss in knee OA.

MR imaging after patellar MACI and MPFL reconstruction: a comparison of isolated versus combined procedures

OBJECTIVE

To qualitatively and quantitatively evaluate the 2.5-year MRI outcome after Matrix-associated autologous chondrocyte implantation (MACI) at the patella, reconstruction of the medial patellofemoral ligament (MPFL), and combined procedures.

METHODS

In 66 consecutive patients (age 22.8 ± 6.4years) with MACI at the patella (n = 16), MPFL reconstruction (MPFL; n = 31), or combined procedures (n = 19) 3T MRI was performed 2.5 years after surgery. For morphological MRI evaluation WORMS and MOCART scores were obtained. In addition quantitative cartilage T2 and T1rho relaxation times were acquired. Several clinical scores were obtained. Statistical analyses included descriptive statistics, Mann-Whitney-U-tests and Pearson correlations.

RESULTS

WORMS scores at follow-up (FU) were significantly worse after combined procedures (8.7 ± 4.9) than after isolated MACI (4.3 ± 3.6, P = 0.005) and after isolated MPFL reconstruction (5.3 ± 5.7, P = 0.004). Bone marrow edema at the patella in the combined group was the only (non-significantly) worsening WORMS parameter from pre- to postoperatively. MOCART scores were significantly worse in the combined group than in the isolated MACI group (57 ± 3 vs 88 ± 9, P < 0.001). Perfect defect filling was achieved in 26% and 69% of cases in the combined and MACI group, respectively (P = 0.031). Global and patellar T2 values were higher in the combined group (Global T2: 34.0 ± 2.8ms) and MACI group (35.5 ± 3.1ms) as compared to the MPFL group (31.1 ± 3.2ms, P < 0.05). T2 values correlated significantly with clinical scores (P < 0.005). Clinical Cincinnati scores were significantly worse in the combined group (P < 0.05).

CONCLUSION

After combined surgery with patellar MACI and MPFL reconstruction inferior MRI outcomes were observed than after isolated procedures. Therefore, patients with need for combined surgery may be at particular risk for osteoarthritis.

Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning

Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected the best-performing models based on the area under the curve (AUC). The study results indicate no significant difference in performance among models using different imaging data. Subsequently, we employed a convolutional neural network (CNN) to extract features from magnetic resonance imaging (MRI), and class activation mapping (CAM) was utilized to generate saliency maps, highlighting regions associated with knee pain severity. A radiologist reviewed the images, identifying specific lesions colocalized with the CAM. The review of 421 knees revealed that effusion/synovitis (30.9%) and cartilage loss (30.6%) were the most frequent abnormalities associated with pain severity. Our study suggests cartilage loss and synovitis/effusion lesions as significant structural factors affecting pain severity in patients with knee osteoarthritis. Furthermore, our study highlights the potential of machine learning for assessing knee pain severity using radiographs.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI

OBJECTIVE

While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals.

METHODS

This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above. Participants without OA at baseline (BL) (Kellgren-Lawrence (KL) ≤ 1) were followed and classified as incident cases (KL ≥ 2 during follow-up; n = 115) and as non-incident (KL ≤ 1 over 96-month; n = 391). Associations between the predictor-variables sex, age, BMI, race, clinical scoring systems, T2 relaxation times and Whole-Organ Magnetic Resonance Imaging-Score (WORMS) readings at BL and the preservation of healthy knees (KL ≤ 1) during a 96-month follow-up period were assessed using logistic regression models.

RESULTS

Obesity and presence of pain showed a significant inverse association with maintaining radiographically normal joints in patients aged 65 and above. T2 relaxation times of the lateral femur and tibia as well as the medial femur were also significantly associated with maintaining radiographically normal knee joints. Additionally, absence of lesions of the lateral meniscus and absence of cartilage lesions in the medial and patellofemoral compartments were significantly associated with maintaining healthy knee joints.

CONCLUSION

Overall, this study provides protective clinical parameters as well as quantitative and semi-quantitative MR-imaging parameters associated with maintaining radiographically normal knee joints in an older population over 8 years.

Development and validation of a frailty index for use in the osteoarthritis initiative

BACKGROUND

The Osteoarthritis Initiative (OAI) evaluates the development and progression of osteoarthritis. Frailty captures the heterogeneity in aging. Use of this resource-intensive dataset to answer aging-related research questions could be enhanced by a frailty measure.

OBJECTIVE

To: (i) develop a deficit accumulation frailty index (FI) for the OAI; (ii) examine its relationship with age and compare between sexes, (iii) validate the FI versus all-cause mortality and (iv) compare this association with mortality with a modified frailty phenotype.

DESIGN

OAI cohort study.

SETTING

North America.

SUBJECTS

An FI was determined for 4,755/4,796 and 4,149/4,796 who had a valid FI and frailty phenotype.

METHODS

Fifty-nine-variables were screened for inclusion. Multivariate Cox regression evaluated the impact of FI or phenotype on all-cause mortality at follow-up (up to 146 months), controlling for age and sex.

RESULTS

Thirty-one items were included. FI scores (0.16 ± 0.09) were higher in older adults and among females (both, P < 0.001). By follow-up, 264 people had died (6.4%). Older age, being male, and greater FI were associated with a higher risk of all-cause mortality (all, P < 0.001). The model including FI was a better fit than the model including the phenotype (AIC: 4,167 vs. 4,178) and was a better predictor of all-cause mortality than the phenotype with an area under receiver operating characteristic curve: 0.652 vs. 0.581.

CONCLUSION

We developed an FI using the OAI and validated it in relation to all-cause mortality. The FI may be used to study aging on clinical, functional and structural aspects of osteoarthritis included in the OAI.

A semi-supervised multiview-MRI network for the detection of Knee Osteoarthritis

Knee OsteoArthritis (OA) is a prevalent chronic condition, affecting a significant proportion of the global population. Detecting knee OA is crucial as the degeneration of the knee joint is irreversible. In this paper, we introduce a semi-supervised multi-view framework and a 3D CNN model for detecting knee OA using 3D Magnetic Resonance Imaging (MRI) scans. We introduce a semi-supervised learning approach combining labeled and unlabeled data to improve the performance and generalizability of the proposed model. Experimental results show the efficacy of our proposed approach in detecting knee OA from 3D MRI scans using a large cohort of 4297 subjects. An ablation study was conducted to investigate the contributions of various components of the proposed model, providing insights into the optimal design of the model. Our results indicate the potential of the proposed approach to improve the accuracy and efficiency of OA diagnosis. The proposed framework reported an AUC of 93.20% for the detection of knee OA.

Inadequate Classification of Poor Response After Total Knee Arthroplasty: A Comparative Analysis of 15 Definitions Using Data From the Dutch Arthroplasty Register and the Osteoarthritis Initiative Database

BACKGROUND

Variations in defining poor response to total knee arthroplasty (TKA) impede comparisons of response after TKA over time and across hospitals. This study aimed to compare the prevalence, overlap, and discriminative accuracy of 15 definitions of poor response after TKA using 2 databases.

METHODS

Data of patients one year after primary TKA from the Dutch Arthroplasty Register (n = 12,275) and the Osteoarthritis Initiative database (n = 204) were used to examine the prevalence, overlap (estimated by Cohen's kappa), and discriminative accuracy (sensitivity, specificity, positive predictive value, negative predictive value, and Youden index) of 15 different definitions of poor response after TKA. In the absence of a gold standard for measuring poor response to TKA, the numeric rating scale satisfaction (≤ 6 'poor responder') and the global assessment of knee impact (dichotomized: ≥ 4 'poor responder') were used as anchors for assessing discriminative accuracy for the Dutch Arthroplasty Register and Osteoarthritis Initiative dataset, respectively. These anchors were chosen based on a prior qualitative study that identified (dis)satisfaction as a central theme of poor responses to TKA by patients and knee specialists.

RESULTS

The median (25th to 75th percentile) prevalence of poor responders in the examined definitions was 18.5% (14.0 to 25.5%), and the median Cohen's kappa for the overlap between pairs of definitions was 0.41 (0.32 to 0.59). Median (25th to 75th percentile) sensitivity was 0.45 (0.39 to 0.54), specificity was 0.86 (0.82 to 0.94), positive predictive value was 0.45 (0.34 to 0.62), negative predictive value was 0.89 (0.87 to 0.89), and the Youden index was 0.36 (0.20 to 0.43).

CONCLUSIONS

This study found a lack of overlap between different definitions of poor response to TKA. None of the examined definitions adequately classified poor responders to TKA. In contrast, the absence of a poor response could be classified with confidence.

Whole-body magnetic resonance imaging at 0.05 Tesla

Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation

Background

Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries.

Methods

A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist's interpretations were employed as the reference for conducting the evaluation.

Results

The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset.

Conclusions

The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.

Effect of weight loss on knee joint synovitis over 48 months and mediation by subcutaneous fat around the knee: data from the Osteoarthritis Initiative

BACKGROUND

Obesity influences the development of osteoarthritis via low-grade inflammation. Progression of local inflammation (= synovitis) increased with weight gain in overweight and obese women compared to stable weight. Synovitis could be associated with subcutaneous fat (SCF) around the knee. Purpose of the study was to investigate the effect of weight loss on synovitis progression and to assess whether SCF around the knee mediates the relationship between weight loss and synovitis progression.

METHODS

We included 234 overweight and obese participants (body mass index [BMI] ≥ 25 kg/m2) from the Osteoarthritis Initiative (OAI) with > 10% weight loss (n = 117) or stable overweight (< ± 3% change, n = 117) over 48 months matched for age and sex. In magnetic resonance imaging (MRI) at baseline and 48 months, effusion-synovitis and Hoffa-synovitis using the MRI Osteoarthritis Knee Score (MOAKS) and average joint-adjacent SCF (ajSCF) were assessed. Odds-ratios (ORs) for synovitis progression over 48 months (≥ 1 score increase) were calculated in logistic regression models adjusting for age, sex, baseline BMI, Physical Activity Scale for the Elderly (PASE), and baseline SCF measurements. Mediation of the effect of weight loss on synovitis progression by local SCF change was assessed.

RESULTS

Odds for effusion-synovitis progression decreased with weight loss and ajSCF decrease (odds ratio [OR] = 0.61 and 0.56 per standard deviation [SD] change, 95% confidence interval [CI] 0.44, 0.83 and 0.40, 0.79, p = 0.002 and 0.001, respectively), whereas odds for Hoffa-synovitis progression increased with weight loss and ajSCF decrease (OR = 1.47 and 1.48, CI 1.05, 2.04 and 1.02, 2.13, p = 0.024 and 0.038, respectively). AjSCF decrease mediated 39% of the effect of weight loss on effusion-synovitis progression.

CONCLUSIONS

Effusion-synovitis progression was slowed by weight loss and decrease in local subcutaneous fat. Hoffa-synovitis characterized by fluid in the infrapatellar fat pad increased at the same time, suggesting a decreasing fat pad rather than active synovitis. Decrease in local subcutaneous fat partially mediated the systemic effect of weight loss on synovitis.

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.

Statin use and longitudinal changes in quantitative MRI-based biomarkers of thigh muscle quality: data from Osteoarthritis Initiative

OBJECTIVE

To assess whether changes in MRI-based measures of thigh muscle quality associated with statin use in participants with and without/at-risk of knee osteoarthritis.

METHODS

This retrospective cohort study used data from the Osteoarthritis Initiative study. Statin users and non-users were matched for relevant covariates using 1:1 propensity-score matching. Participants were further stratified according to baseline radiographic knee osteoarthritis status. We used a validated deep-learning method for thigh muscle MRI segmentation and calculation of muscle quality biomarkers at baseline, 2nd, and 4th visits. Mean difference and 95% confidence intervals (CI) in longitudinal 4-year measurements of muscle quality biomarkers, including cross-sectional area, intramuscular adipose tissue, contractile percent, and knee extensors and flexors maximum and specific contractile force (force/muscle area) were the outcomes of interest.

RESULTS

After matching, 3772 thighs of 1910 participants were included (1886 thighs of statin-users: 1886 of non-users; age: 62 ± 9 years (average ± standard deviation), range: 45-79; female/male: 1). During 4 years, statin use was associated with a slight decrease in muscle quality, indicated by decreased knee extension maximum (mean-difference, 95% CI: - 1.85 N/year, - 3.23 to - 0.47) and specific contractile force (- 0.04 N/cm2/year, - 0.07 to - 0.01), decreased thigh muscle contractile percent (- 0.03%/year, - 0.06 to - 0.01), and increased thigh intramuscular adipose tissue (3.06 mm2/year, 0.53 to 5.59). Stratified analyses showed decreased muscle quality only in participants without/at-risk of knee osteoarthritis but not those with established knee osteoarthritis.

CONCLUSIONS

Statin use is associated with a slight decrease in MRI-based measures of thigh muscle quality over 4 years. However, considering statins' substantial cardiovascular benefits, these slight muscle changes may be relatively less important in overall patient care.

Opportunistic Screening of Low Bone Mineral Density From Standard X-Rays

BACKGROUND

Osteoporosis, characterized by loss of bone mineral density (BMD), is underscreened. Osteoporosis and low bone mass are diagnosed by a BMD T-score ≤ -2.5, and between -1.0 and -2.5, respectively, at the femoral neck or lumbar vertebrae (L1-4), using dual energy x-ray absorptiometry (DXA). The ability to estimate BMD at those anatomic sites from standard radiographs would enable opportunistic screening of low BMD (T-score < -1) in individuals undergoing x-ray for any clinical indication.

METHODS

Radiographs of the lumbar spine, thoracic spine, chest, pelvis, hand, and knee, with a paired DXA acquired within 1 year, were obtained from community imaging centers (62,023 x-ray-DXA pairs of patients). A software program called Rho was developed that uses x-ray, age, and sex as inputs, and outputs a score of 1 to 10 that corresponds with the likelihood of low BMD. The program's performance was assessed using receiver-operating characteristic analyses in three independent test sets, as follows: patients from community imaging centers (n = 3,729; 83% female); patients in the Canadian Multicentre Osteoporosis Study (n = 1,780; 71% female); and patients in the Osteoarthritis Initiative (n = 591; 50% female).

RESULTS

The areas under the receiver-operating characteristic curves were 0.89 (0.87-0.90), 0.87 (0.85-0.88), and 0.82 (0.79-0.85), respectively, and subset analyses showed similar results for each sex, body part, and race.

CONCLUSION

Rho can opportunistically screen patients at risk of low BMD (at femoral neck or L1-4) from radiographs of the lumbar spine, thoracic spine, chest, pelvis, hand, or knee.

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative

In medical image analysis, automated segmentation of multi-component anatomical entities, with the possible presence of variable anomalies or pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based models to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images in individuals with varying grades of knee osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, U-Net-based models were developed to reconstruct bone volume profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. An anomaly-aware segmentation network, which was compared to anomaly-naïve segmentation networks, was used to provide a final automated segmentation of the individual femoral, tibial and patellar bone and cartilage volumes from the knee MR images which contain a spectrum of bone anomalies. The anomaly-aware segmentation approach provided up to 58% reduction in Hausdorff distances for bone segmentations compared to the results from anomaly-naïve segmentation networks. In addition, the anomaly-aware networks were able to detect bone anomalies in the MR images with greater sensitivity and specificity (area under the receiver operating characteristic curve [AUC] up to 0.896) compared to anomaly-naïve segmentation networks (AUC up to 0.874).

Dense Multi-Scale Graph Convolutional Network for Knee Joint Cartilage Segmentation

In this paper, we propose a dense multi-scale adaptive graph convolutional network (DMA-GCN) method for automatic segmentation of the knee joint cartilage from MR images. Under the multi-atlas setting, the suggested approach exhibits several novelties, as described in the following. First, our models integrate both local-level and global-level learning simultaneously. The local learning task aggregates spatial contextual information from aligned spatial neighborhoods of nodes, at multiple scales, while global learning explores pairwise affinities between nodes, located globally at different positions in the image. We propose two different structures of building models, whereby the local and global convolutional units are combined by following an alternating or a sequential manner. Secondly, based on the previous models, we develop the DMA-GCN network, by utilizing a densely connected architecture with residual skip connections. This is a deeper GCN structure, expanded over different block layers, thus being capable of providing more expressive node feature representations. Third, all units pertaining to the overall network are equipped with their individual adaptive graph learning mechanism, which allows the graph structures to be automatically learned during training. The proposed cartilage segmentation method is evaluated on the entire publicly available Osteoarthritis Initiative (OAI) cohort. To this end, we have devised a thorough experimental setup, with the goal of investigating the effect of several factors of our approach on the classification rates. Furthermore, we present exhaustive comparative results, considering traditional existing methods, six deep learning segmentation methods, and seven graph-based convolution methods, including the currently most representative models from this field. The obtained results demonstrate that the DMA-GCN outperforms all competing methods across all evaluation measures, providing DSC=95.71% and DSC=94.02% for the segmentation of femoral and tibial cartilage, respectively.

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.

Promoting fast MR imaging pipeline by full-stack AI

Magnetic resonance imaging (MRI) is a widely used imaging modality in clinics for medical disease diagnosis, staging, and follow-up. Deep learning has been extensively used to accelerate k-space data acquisition, enhance MR image reconstruction, and automate tissue segmentation. However, these three tasks are usually treated as independent tasks and optimized for evaluation by radiologists, thus ignoring the strong dependencies among them; this may be suboptimal for downstream intelligent processing. Here, we present a novel paradigm, full-stack learning (FSL), which can simultaneously solve these three tasks by considering the overall imaging process and leverage the strong dependence among them to further improve each task, significantly boosting the efficiency and efficacy of practical MRI workflows. Experimental results obtained on multiple open MR datasets validate the superiority of FSL over existing state-of-the-art methods on each task. FSL has great potential to optimize the practical workflow of MRI for medical diagnosis and radiotherapy.

Rapid whole brain 3D T2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability

PURPOSE

To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation.

METHODS

A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T.

RESULTS

The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping.

CONCLUSION

The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.

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.

CartiMorph: A framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

The association between anterior cruciate ligament degeneration and incident knee osteoarthritis: Data from the osteoarthritis initiative

Background

Though anterior cruciate ligament (ACL) tear has been widely accepted as an important accelerator for knee osteoarthritis (KOA), the role of intrinsic ACL degeneration in developing KOA has not been fully investigated.

Purpose

To determine whether ACL degeneration, in the absence of ACL tear, is associated with incident KOA over 4 years.

Study design

Cohort study; Level of evidence, 2.

Methods

Participants' knees in this nested case-control study were selected from the Osteoarthritis Initiative (OAI) study, with Kellgren-Lawrence grading (Kellgren-Lawrence grading) of 0 or 1 ​at baseline (BL). Case knees which had incident KOA (KLG ≥2) over 4 years, were matched 1:1 with control knees by gender, age and radiographic status. ACL signal intensity alteration (0-3 scale) and volume were assessed as compositional feature and morphology of ACL degeneration, using knee MRI at P0 (time of onset of incident KOA), P-1 (1 year prior to P0) and baseline. Conditional logistic regression was applied to analyze the association between measures of ACL degeneration and incident KOA.

Results

337 case knees with incident KOA were matched to 337 control knees. Participants were mostly female (68.5%), with an average age of 59.9 years old. ACL signal intensity alterations at BL, P-1 and P0 were significantly associated with an increased odds of incident KOA respectively (all P for trend ≤0.001). In contrast, ACL volumes were not significantly associated with incident KOA at any time points.

Conclusions

ACL signal intensity alteration is associated with increased incident KOA over 4 years, whereas ACL volume is not.The translational potential of this article: This paper focused on ACL signal intensity alteration which could better reflect ACL degeneration rather than ACL tear during the progression of KOA and explored this topic in a nested case-control study. Utilizing MR images from KOA participants, we extracted the imaging features of ACL. In addition, we established a semi-quantitative score for ACL signal intensity alteration and found a significant correlation between it and KOA incidence. Our findings confirmed that the more severe the ACL signal intensity alteration, the stronger relationship with the occurrence of KOA. This suggests that more emphasis should be placed on ACL degeneration rather than ACL integrity in the future.

Machine learning and deep neural network-based learning in osteoarthritis knee

Osteoarthritis (OA) of the knee joint is considered the commonest musculoskeletal condition leading to marked disability for patients residing in various regions around the globe. Application of machine learning (ML) in doing research regarding OA has brought about various clinical advances viz, OA being diagnosed at preliminary stages, prediction of chances of development of OA among the population, discovering various phenotypes of OA, calculating the severity in OA structure and also discovering people with slow and fast progression of disease pathology, etc. Various publications are available regarding machine learning methods for the early detection of osteoarthritis. The key features are detected by morphology, molecular architecture, and electrical and mechanical functions. In addition, this particular technique was utilized to assess non-interfering, non-ionizing, and in-vivo techniques using magnetic resonance imaging. ML is being utilized in OA, chiefly with the formulation of large cohorts viz, the OA Initiative, a cohort observational study, the Multi-centre Osteoarthritis Study, an observational, prospective longitudinal study and the Cohort Hip & Cohort Knee, an observational cohort prospective study of both hip and knee OA. Though ML has various contributions and enhancing applications, it remains an imminent field with high potential, also with its limitations. Many more studies are to be carried out to find more about the link between machine learning and knee osteoarthritis, which would help in the improvement of making decisions clinically, and expedite the necessary interventions.

Frailty predicts knee pain trajectory over 9 years: results from the Osteoarthritis Initiative

OBJECTIVE

Frailty is a multisystem syndrome and its relationship with symptomatic osteoarthritis has been reported. We aimed to identify trajectories of knee pain in a large prospective cohort and to describe the effect of frailty status at baseline on the pain trajectories over 9 years.

METHODS

We included 4419 participants (mean age 61.3 years, 58% female) from the Osteoarthritis Initiative cohort. Participants were classified as "no frailty," "pre-frailty," or "frailty" at baseline, based on 5 characteristics (ie, unintentional weight loss, exhaustion, weak energy, slow gait speed, and low physical activity). Knee pain was evaluated annually using the Western Ontario and McMaster Universities Osteoarthritis Index pain subscale (0-20) from baseline to 9 years.

RESULTS

Of the participants included, 38.4%, 55.4%, and 6.3% were classified as "no frailty," "pre-frailty," and "frailty," respectively. Five pain trajectories were identified: "No pain" (n = 1010, 22.8%), "Mild pain" (n = 1656, 37.3%), "Moderate pain" (n = 1149, 26.0%), "Severe pain" (n = 477, 10.9%), and "Very Severe pain" (n = 127, 3.0%). Compared to participants with no frailty, those with pre-frailty and frailty were more likely to have more severe pain trajectories (pre-frailty: odds ratios [ORs] 1.5 to 2.1; frailty: ORs 1.5 to 5.0), after adjusting for potential confounders. Further analyses indicated that the associations between frailty and pain were mainly driven by exhaustion, slow gait speed, and weak energy.

CONCLUSIONS

Approximately two-thirds of middle-aged and older adults were frail or pre-frail. The role of frailty in predicting pain trajectories suggests that frailty may be an important treatment target for knee pain.

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.

Associations between weight change, knee subcutaneous fat and cartilage thickness in overweight and obese individuals: 4-Year data from the osteoarthritis initiative

OBJECTIVE

To assess (i) the impact of changes in body weight on changes in joint-adjacent subcutaneous fat (SCF) and cartilage thickness over 4 years and (ii) the relation between changes in joint-adjacent SCF and knee cartilage thickness.

DESIGN

Individuals from the Osteoarthritis Initiative (total=399) with > 10% weight gain (n=100) and > 10% weight loss (n=100) over 4 years were compared to a matched control cohort with less than 3% change in weight (n=199). 3.0T Magnetic Resonance Imaging (MRI) of the right knee was performed at baseline and after 4 years to quantify joint-adjacent SCF and cartilage thickness. Linear regression models were used to evaluate the associations between the (i) weight change group and 4-year changes in both knee SCF and cartilage thickness, and (ii) 4-year changes in knee SCF and in cartilage thickness. Analyses were adjusted for age, sex, baseline body mass index (BMI), tibial diameter (and weight change group in analysis (ii)).

RESULTS

Individuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p < 0.001) than controls; individuals who gained weight had significantly greater joint-adjacent SCF than controls (beta range: -1.4 to -3.9 mm, p < 0.001). No statistically significant associations were found between weight change and cartilage thickness change. However, increases in joint-adjacent SCF over 4 years were significantly associated with decreases in cartilage thickness (p = 0.04).

CONCLUSIONS

Weight change was associated with joint-adjacent SCF, but not with change in cartilage thickness. However, 4-year increases in joint-adjacent SCF were associated with decreases in cartilage thickness independent of baseline BMI and weight change group.

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.

Meniscal degeneration is prognostic of destabilzing meniscal tear and accelerated knee osteoarthritis: Data from the Osteoarthritis Initiative

The objective of this study was to assess the prognostic potential of magnetic resonance (MR)-detected meniscal degeneration in relation to incident destabilizing meniscal tears (radial, complex, root, or macerated) or accelerated knee osteoarthritis (AKOA). We used existing MR data from a case-control study of three groups from the Osteoarthritis Initiative without radiographic KOA at baseline: AKOA, typical KOA, and no KOA. From these groups, we included people without medial and lateral meniscal tear at baseline (n = 226) and 48-month meniscal data (n = 221). Intermediate-weighted fat-suppressed MR images annually from baseline to the 48-month visit were graded using a semiquantitative meniscal tear classification criterion. Incident destabilizing meniscal tear was defined as progressing from an intact meniscus to a destabilizing tear by the 48-month visit. We used two logistic regression models to assess whether: (1) presence of medial meniscal degeneration was associated with an incident medial destabilizing meniscal tear, and (2) presence of meniscal degeneration in either meniscus was associated with incident AKOA over the next 4 years. People with the presence of a medial meniscal degeneration had three times the odds of developing an incident destabilizing medial meniscal tear within 4 years compared with a person without medial meniscus degeneration (odds ratio [OR]: 3.03; 95% confidence interval [CI]: 1.40-6.59). People with meniscal degeneration had five times the odds of developing incident AKOA within 4 years compared with a person without meniscal degeneration in either meniscus (OR: 5.04; 95% CI: 2.57-9.89). Meniscal degeneration on MR is clinically meaningful as it relates to future poor outcomes.

Association of quantitative measures of medial meniscal extrusion with structural and symptomatic knee osteoarthritis progression - Data from the OAI FNIH biomarker study

OBJECTIVE

To study the association of quantitative medial meniscal position measures with radiographic and symptomatic knee osteoarthritis (OA) progression over 2-4 years.

METHODS

The FNIH OAI Biomarkers study comprised 600 participants in four subgroups: 194 case knees with combined structural (medial minimum joint space width (minJSW) loss ≥0.7 mm) and symptomatic (persistent Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale increase ≥9 [0-100 scale]) progression; 200 knees with neither structural nor symptomatic progression; 103 knees with isolated structural and 103 with isolated symptomatic progression. Coronal double echo at steady state (DESS) MRIs were used for segmenting five central slices of the medial meniscus. Associations with progression were examined using logistic regression (adjusted for demographic and clinical data).

RESULTS

Greater baseline medial meniscal extrusion was associated with combined structural/symptomatic progression (OR 1.59; 95%CI: [1.25,2.04]). No relationship was observed for tibial plateau coverage or meniscal overlap distance. The two-year increase in meniscal extrusion (OR 1.48 [1.21, 1.83]), and reduction in tibial plateau coverage (OR 0.70 [0.58,0.86]) and overlap distance (OR 0.73 [0.60,0.89]) were associated with combined progression. Greater baseline extrusion was associated with isolated structural and less extrusion with isolated symptomatic progression. The longitudinal increase in meniscal extrusion, and reduction in tibial plateau coverage and overlap distance were associated with structural, but not with symptomatic progression.

CONCLUSION

Baseline measures of medial meniscal extrusion were consistently positively associated with combined radiographic/symptomatic progression and with isolated structural, but not with isolated symptomatic progression. These measures may therefore allow one to assess the risk of structural knee OA progression and to monitor interventions restoring meniscal position and function.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies

PURPOSE

To compare the evaluation metrics for deep learning methods that were developed using imbalanced imaging data in osteoarthritis studies.

MATERIALS AND METHODS

This retrospective study utilized 2996 sagittal intermediate-weighted fat-suppressed knee MRIs with MRI Osteoarthritis Knee Score readings from 2467 participants in the Osteoarthritis Initiative study. We obtained probabilities of the presence of bone marrow lesions (BMLs) from MRIs in the testing dataset at the sub-region (15 sub-regions), compartment, and whole-knee levels based on the trained deep learning models. We compared different evaluation metrics (e.g., receiver operating characteristic (ROC) and precision-recall (PR) curves) in the testing dataset with various class ratios (presence of BMLs vs. absence of BMLs) at these three data levels to assess the model's performance.

RESULTS

In a subregion with an extremely high imbalance ratio, the model achieved a ROC-AUC of 0.84, a PR-AUC of 0.10, a sensitivity of 0, and a specificity of 1.

CONCLUSION

The commonly used ROC curve is not sufficiently informative, especially in the case of imbalanced data. We provide the following practical suggestions based on our data analysis: 1) ROC-AUC is recommended for balanced data, 2) PR-AUC should be used for moderately imbalanced data (i.e., when the proportion of the minor class is above 5% and less than 50%), and 3) for severely imbalanced data (i.e., when the proportion of the minor class is below 5%), it is not practical to apply a deep learning model, even with the application of techniques addressing imbalanced data issues.

Which risk factors determine cartilage thickness and composition change in radiographically normal knees? - Data from the Osteoarthritis Initiative

Objective

Therapy for osteoarthritis ideally aims at preserving structure before radiographic change occurs. This study tests: a) whether longitudinal deterioration in cartilage thickness and composition (transverse relaxation-time T2) are greater in radiographically normal knees "at risk" of incident osteoarthritis than in those without risk factors; and b) which risk factors may be associated with these deteriorations.

Design

755 knees from the Osteoarthritis Initiative were studied; all were bilaterally Kellgren Lawrence grade [KLG] 0 initially, and had magnetic resonance images available at 12- and 48-month follow-up. 678 knees were "at risk", whereas 77 were not (i.e., non-exposed reference). Cartilage thickness and composition change was determined in 16 femorotibial subregions, with deep and superficial T2 being analyzed in a subset (n ​= ​59/52). Subregion values were used to compute location-independent change scores.

Results

In KLG0 knees "at risk", the femorotibial cartilage thinning score (-634 ​± ​516 ​μm) over 3 years exceeded the thickening score by approximately 20%, and was 27% greater (p ​< ​0.01; Cohen D -0.27) than the thinning score in "non-exposed" knees (-501 ​± ​319 ​μm). Superficial and deep cartilage T2 change, however, did not differ significantly between both groups (p ​≥ ​0.38). Age, sex, body mass index, knee trauma/surgery history, family history of joint replacement, presence of Heberden's nodes, repetitive knee bending were not significantly associated with cartilage thinning (r²<1%), with only knee pain reaching statistical significance.

Conclusions

Knees "at risk" of incident knee OA displayed greater cartilage thinning scores than those "non-exposed". Except for knee pain, the greater cartilage loss was not significantly associated with demographic or clinical risk factors.

Osteoarthritis year in review 2022: imaging

PURPOSE

This narrative review summarizes original research focusing on imaging in osteoarthritis (OA) published between April 1st 2021 and March 31st 2022. We only considered English publications that were in vivo human studies.

METHODS

The PubMed, Medline, Embase, Scopus, and ISI Web of Science databases were searched for "Osteoarthritis/OA" studies based on the search terms: "Radiography", "Ultrasound/US", "Computed Tomography/CT", "DXA", "Magnetic Resonance Imaging/MRI", "Artificial Intelligence/AI", and "Deep Learning". This review highlights the anatomical focus of research on the structures within the tibiofemoral, patellofemoral, hip, and hand joints. There is also a noted focus on artificial intelligence applications in OA imaging.

RESULTS

Over the last decade, the increasing trend of using open-access large databases has reached a plateau (from 17 to 37). Compositional MRI has had the most prominent use in OA imaging and its biomarkers have been used in the detection of preclinical OA and prediction of OA outcomes. Most noteworthy, there has been an accelerated rate of publications on the implications of artificial intelligence, used in developing prediction models and performing trabecular texture analysis, in OA imaging (from 17 to 154).

CONCLUSIONS

While imaging has maintained its key role in OA research, publication trends have shown an emphasis on the integration of AI. During the past year, MRI has maintained the highest prevalence in usage while US and CT remain as readily available modalities. Finally, there has been a notable uptake in the development and validation of AI techniques used to perform texture analysis and predict OA progression.

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.

Comparative validation of the knee inflammation MRI scoring system and the MRI osteoarthritis knee score for semi-quantitative assessment of bone marrow lesions and synovitis-effusion in osteoarthritis: an international multi-reader exercise

Background

Bone marrow lesions (BMLs) and synovitis on magnetic resonance imaging (MRI) are associated with symptoms and predict degeneration of articular cartilage in osteoarthritis (OA). Validated methods for their semiquantitative assessment on MRI are available, but they all have similar scoring designs and questionable sensitivity to change. New scoring methods with completely different designs need to be developed and compared to existing methods.

Objectives

To compare the performance of new web-based versions of the Knee Inflammation MRI Scoring System (KIMRISS) with the MRI OA Knee Score (MOAKS) for quantification of BMLs and synovitis-effusion (S-E).

Design

Retrospective follow-up cohort.

Methods

We designed web-based overlays outlining regions in the knee that are scored for BML in MOAKS and KIMRISS. For KIMRISS, both BML and S-E are scored on consecutive sagittal slices. The performance of these methods was compared in an international reading exercise of 8 readers evaluating 60 pairs of scans conducted 1 year apart from cases recruited to the OA Initiative (OAI) cohort. Interobserver reliability for baseline status and baseline to 1 year change in BML and S-E was assessed by intra-class correlation coefficient (ICC) and smallest detectable change (SDC). Feasibility was assessed using the System Usability Scale (SUS).

Results

Mean change in BML and S-E was minimal over 1 year. Pre-specified targets for acceptable reliability (ICC ⩾ 0.80 and ⩾ 0.70 for status and change scores, respectively) were achieved more frequently for KIMRISS for both BML and synovitis. Mean (95% CI) ICC for change in BML was 0.88 (0.83-0.92) and 0.69 (0.60-0.78) for KIMRISS and MOAKS, respectively. KIMRISS mean SUS usability score was 85.7 and at the 95th centile of ranking for usability versus a score of 55.4 and 20th centile for MOAKS.

Conclusion

KIMRISS had superior performance metrics to MOAKS for quantification of BML and S-E. Both methods should be further compared in trials of new therapies for OA.

Machine learning as an adjunct to expert observation in classification of radiographic knee osteoarthritis: findings from the Hertfordshire Cohort Study

BACKGROUND

Osteoarthritis is the most prevalent type of arthritis. Many approaches exist for characterising radiographic knee OA, including machine learning (ML).

AIMS

To examine Kellgren and Lawrence (K&L) scores from ML and expert observation, minimum joint space and osteophyte in relation to pain and function.

METHODS

Participants from the Hertfordshire Cohort Study, comprising individuals born in Hertfordshire from 1931 to 1939, were analysed. Radiographs were assessed by clinicians and ML (convolutional neural networks) for K&L scoring. Medial minimum joint space and osteophyte area were ascertained using the knee OA computer-aided diagnosis (KOACAD) program. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was administered. Receiver operating characteristic analysis was implemented for minimum joint space, osteophyte, and observer- and ML-derived K&L scores in relation to pain (WOMAC pain score > 0) and impaired function (WOMAC function score > 0).

RESULTS

359 participants (aged 71-80) were analysed. Among both sexes, discriminative capacity regarding pain and function was fairly high for observer-derived K&L scores [area under curve (AUC): 0.65 (95% CI 0.57, 0.72) to 0.70 (0.63, 0.77)]; results were similar among women for ML-derived K&L scores. Discriminative capacity was moderate among men for minimum joint space in relation to pain [0.60 (0.51, 0.67)] and function [0.62 (0.54, 0.69)]. AUC < 0.60 for other sex-specific associations.

DISCUSSION

Observer-derived K&L scores had higher discriminative capacity regarding pain and function compared to minimum joint space and osteophyte. Among women, discriminative capacity was similar for observer- and ML-derived K&L scores.

CONCLUSION

ML as an adjunct to expert observation for K&L scoring may be beneficial due to the efficiency and objectivity of ML.

Prediction of total knee replacement using deep learning analysis of knee MRI

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case-control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs.

Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks

Background: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility. Moreover, while such algorithms have been investigated for other anatomies, they are largely unexplored for musculoskeletal applications such as RA, and efforts to understand trained models and improve trust in their predictions have been limited in medical imaging. Methods: A dataset of 27 RA patients was used to train algorithms that synthetically generated post-Gd IDEAL wrist coronal T₁-weighted scans from pre-contrast scans. UNets and PatchGANs were trained, leveraging an anomaly-weighted L₁ loss and global generative adversarial network (GAN) loss for the PatchGAN. Occlusion and uncertainty maps were also generated to understand model performance. Results: UNet synthetic post-contrast images exhibited stronger normalized root mean square error (nRMSE) than PatchGAN in full volumes and the wrist, but PatchGAN outperformed UNet in synovial joints (UNet nRMSEs: volume = 6.29 ± 0.88, wrist = 4.36 ± 0.60, synovial = 26.18 ± 7.45; PatchGAN nRMSEs: volume = 6.72 ± 0.81, wrist = 6.07 ± 1.22, synovial = 23.14 ± 7.37; n = 7). Occlusion maps showed that synovial joints made substantial contributions to PatchGAN and UNet predictions, while uncertainty maps showed that PatchGAN predictions were more confident within those joints. Conclusions: Both pipelines showed promising performance in synthesizing post-contrast images, but PatchGAN performance was stronger and more confident within synovial joints, where an algorithm like this would have maximal clinical utility. Image synthesis approaches are therefore promising for RA and synthetic inflammatory imaging.

Levothyroxine use and longitudinal changes in thigh muscles in at-risk participants for knee osteoarthritis: preliminary analysis from Osteoarthritis Initiative cohort

BACKGROUND

We examined the association between levothyroxine use and longitudinal MRI biomarkers for thigh muscle mass and composition in at-risk participants for knee osteoarthritis (KOA) and their mediatory role in subsequent KOA incidence.

METHODS

Using the Osteoarthritis Initiative (OAI) data, we included the thighs and corresponding knees of participants at risk but without established radiographic KOA (baseline Kellgren-Lawrence grade (KL) < 2). Levothyroxine users were defined as self-reported use at all annual follow-up visits until the 4th year and were matched with levothyroxine non-users for potential confounders (KOA risk factors, comorbidities, and relevant medications covariates) using 1:2/3 propensity score (PS) matching. Using a previously developed and validated deep learning method for thigh segmentation, we assessed the association between levothyroxine use and 4-year longitudinal changes in muscle mass, including cross-sectional area (CSA) and muscle composition biomarkers including intra-MAT (within-muscle fat), contractile percentage (non-fat muscle CSA/total muscle CSA), and specific force (force per CSA). We further assessed whether levothyroxine use is associated with an 8-year risk of standard KOA radiographic (KL ≥ 2) and symptomatic incidence (incidence of radiographic KOA and pain on most of the days in the past 12 months). Finally, using a mediation analysis, we assessed whether the association between levothyroxine use and KOA incidence is mediated via muscle changes.

RESULTS

We included 1043 matched thighs/knees (266:777 levothyroxine users:non-users; average ± SD age: 61 ± 9 years, female/male: 4). Levothyroxine use was associated with decreased quadriceps CSAs (mean difference, 95%CI: - 16.06 mm²/year, - 26.70 to - 5.41) but not thigh muscles' composition (e.g., intra-MAT). Levothyroxine use was also associated with an increased 8-year risk of radiographic (hazard ratio (HR), 95%CI: 1.78, 1.15-2.75) and symptomatic KOA incidence (HR, 95%CI: 1.93, 1.19-3.13). Mediation analysis showed that a decrease in quadriceps mass (i.e., CSA) partially mediated the increased risk of KOA incidence associated with levothyroxine use.

CONCLUSIONS

Our exploratory analyses suggest that levothyroxine use may be associated with loss of quadriceps muscle mass, which may also partially mediate the increased risk of subsequent KOA incidence. Study interpretation should consider underlying thyroid function as a potential confounder or effect modifier. Therefore, future studies are warranted to investigate the underlying thyroid function biomarkers for longitudinal changes in the thigh muscles.

Effects of Weight Change on Knee and Hip Radiographic Measurements and Pain Over Four Years: Data From the Osteoarthritis Initiative

OBJECTIVE

To assess the effects of weight loss and weight gain on hip and knee radiographic changes, pain, and joint replacement over 4 years.

METHODS

Participants (n = 2,752) from the Osteoarthritis Initiative were classified as those with weight gain (more than 5% gain), weight loss (more than -5% loss), or as controls (-3% to 3% change) over 4 years. Generalized estimating equations (adjusted for age, sex, and body mass index) were used to assess the relationship between the weight-change group and 4-year changes in knee radiographic osteoarthritis (OA) (Kellgren/Lawrence [K/L] grade), hip OA (Croft summary grade), joint space narrowing (JSN), and joint pain.

RESULTS

For radiographic knee OA, weight loss was associated with significantly lower odds of K/L grade worsening over 4 years (odds ratio [OR] 0.69 [95% confidence interval (95% CI) 0.53-0.91], P = 0.009), and weight gain was significantly associated with higher odds of medial knee JSN (OR 1.29 [95% CI 1.01-1.64], P = 0.038) compared to controls. For knee pain, weight loss was significantly associated with knee pain resolution over 4 years (OR 1.40 [95% CI 1.06-1.86], P = 0.019) while weight gain was associated with knee pain development (OR 1.34 [95% CI 1.08-1.67], P = 0.009) compared to controls. For all hip outcomes, no significant associations (P > 0.05) were found with weight-change groups. The associations between the weight-change group and total hip or total knee replacement were not significant (P > 0.05).

CONCLUSION

This large, longitudinal study (n = 2,752 with 4-year follow-up) suggests that weight loss may protect against, and weight gain may exacerbate, radiographic and symptomatic knee OA, while weight change (at a 5% threshold) does not have significant effects on hip OA.

Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium

BACKGROUND

Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.

METHODS

A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.

RESULTS

Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.

CONCLUSION

Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.

Lateral meniscal body extrusion is associated with MRI-defined knee structural damage progression over 4 years: Data from the osteoarthritis initiative

PURPOSE

To investigate the association of lateral meniscal body extrusion (LMBE) with OA-related knee structural damage progression over 4 years and to obtain thresholds of LMBE in predicting knee structural damage progression.

METHOD

We studied 196 subjects (67 male) with a mean age of 59.5 ± 7.9 (SD) years (range45-78 years) that were randomly selected from the Osteoarthritis Initiative. Radiological assessment was performed using the Osteoarthritis Research Society International grading system. Baseline LMBE was quantified on coronal sections of intermediate-weighted MRI sequences obtained at 3.0 T scanner. Knee structural damages were measured using the whole-organ MRI score. Linear regression analysis and binary logistic regression analysis was used to assess the correlation between LMBE and knee structural damage. ROC analysis and Youden index were used for calculating thresholds.

RESULTS

Cross-sectionally, increased LMBE was significantly associated with more severe lateral meniscal damage, patellofemoral and lateral compartment cartilage damage, and posterior cruciate ligament damage. Longitudinally, LMBE was statistically associated with progression of lateral meniscal damage, lateral compartment cartilage damage and bone marrow edema patterns, posterior cruciate ligament lesions and popliteal cysts. The thresholds of LMBE in suggesting progression of lateral meniscal lesions and lateral compartment cartilage lesions were 1.4 mm and 1.3 mm, respectively.

CONCLUSION

Baseline LMBE was associated with structural damage progression over 4 years in knees with or at risks for OA. Thresholds of 1.4 mm and 1.3 mm could be used in predicting progression over 4 years of lateral meniscal damage and lateral compartment cartilage damage, respectively.

Medial congruent polyethylene design show different tibiofemoral kinematics and enhanced congruency compared to a standard symmetrical cruciate retaining design for total knee arthroplasty-an in vivo randomized controlled study of gait using dynamic radiostereometry

PURPOSE

New total knee arthroplasty implant designs attempt to normalize kinematics patterns that may improve functional performance and patient satisfaction. It was hypothesized that a more medial congruent (MC) anatomic bearing design (1) influences the tibiofemoral kinematics and (2) enhances articular congruency compared to a standard symmetrical cruciate retaining (CR) bearing design.

METHODS

In this double-blinded randomized study, 66 patients with knee osteoarthritis were randomly included in two groups: MC (n = 31) and CR (n = 33). Clinical characteristics such as knee ligament lesions and knee osteoarthritis scores were graded on preoperative magnetic resonance imaging and radiography. At the 1-year follow-up, dynamic radiostereometric analysis was used to assess tibiofemoral joint kinematics and articulation congruency. Patient-reported outcome measures, Oxford Knee Score, the Forgotten Joint Score, and the Knee Osteoarthritis Outcome Score, were assessed preoperatively and at the 1-year follow-up.

RESULTS

Compared to the CR bearing, the MC bearing displayed an offset with approximately 3 mm greater anterior tibial drawer (p < 0.001) during the entire motion, and up to approximately 3.5 degrees more tibial external rotation (p = 0.004) from mid-swing to the end of the gait cycle at the 1-year follow-up. Furthermore, the congruency area in the joint articulation was larger during approximately 80% of the gait cycle for the MC bearing compared to the CR. The patient-reported outcome measures improved (p < 0.001), but there were no differences between groups. In addition, there were no differences in clinical characteristics and there were no knee revisions or recognized deep infections during follow-up.

CONCLUSION

The study demonstrates that the MC-bearing design changes tibiofemoral kinematics and increases the area of congruency towards more native knee kinematics than the CR bearing. In perspective this may contribute to a more stabilized knee motion, restoring the patient's confidence in knee function during daily activities.

Worsening of Articular Tissue Damage as Defined by Semi-Quantitative MRI Is Associated With Concurrent Quantitative Cartilage Loss Over 24 Months

OBJECTIVE

To assess the association of worsening of magnetic resonance imaging (MRI) semi-quantitative (SQ) tissue features with concurrent change in quantitative (Q) cartilage thickness measurements over 24 months within the Foundation for the National Institutes of Health (FNIH) Biomarker Consortium study.

METHODS

In all, 599 participants were included. SQ assessment included cartilage damage, meniscal extrusion and damage, osteophytes, bone marrow lesions (BMLs), and effusion- and Hoffa-synovitis. Change in medial compartment Q cartilage thickness was stratified by concurrent ipsicompartmental SQ changes. Between-group comparisons were performed using analysis of covariance (ANCOVA) with adjustment for age, sex, and body mass index (BMI). Results were presented as adjusted mean difference.

RESULTS

Knees with any increase in SQ cartilage scores in the medial compartment (n = 268) showed more Q cartilage loss compared to knees that remained stable (mean adjusted difference [MAD] = -0.16 mm, 95% confidence interval [CI]: [-0.19, -0.13] mm). Knees with any increase in meniscal extrusion in the medial compartment (n = 98) showed more Q cartilage loss than knees without (MAD = -0.18 mm, 95% CI: [-0.22, -0.14] mm. Comparable findings were seen for meniscal damage worsening. Regarding BMLs, an increase by one subregion resulted in a MAD of Q cartilage loss of -0.10 mm, 95% CI: [-0.14, -0.06] mm, while this effect almost tripled for change in two or more subregions. Increase in either effusion- and/or Hoffa-synovitis by one grade resulted in a MAD of -0.07 mm, 95% CI: [-0.10, -0.03] mm.

CONCLUSION

Worsening of SQ cartilage damage, meniscal extrusion and damage, number of subregions affected by BML, maximum size of BMLs and worsening of effusion- and/or Hoffa synovitis is associated with increased Q cartilage loss.