T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative

Clinical prediction models for knee pain in patients with knee osteoarthritis: a systematic review

OBJECTIVE

To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis.

METHODS

The electronic databases PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library were searched from their inception to May 2023 for any studies to develop and validate a prediction model for predicting knee pain in patients with knee osteoarthritis. Two reviewers independently screened titles, abstracts, and full-text qualifications, and extracted data. Risk of bias was assessed using the PROBAST. Data extraction of eligible articles was extracted by a data extraction form based on CHARMS. The quality of evidence was graded according to GRADE. The results were summarized with descriptive statistics.

RESULTS

The search identified 2693 records. Sixteen articles reporting on 26 prediction models were included targeting occurrence (n = 9), others (n = 7), progression (n = 5), persistent (n = 2), incident (n = 1), frequent (n = 1), and flares (n = 1) of knee pain. Most of the studies (94%) were at high risk of bias. Model discrimination was assessed by the AUROC ranging from 0.62 to 0.81. The most common predictors were age, BMI, gender, baseline pain, and joint space width. Only frequent knee pain had a moderate quality of evidence; all other types of knee pain had a low quality of evidence.

CONCLUSION

There are many prediction models for knee pain in patients with knee osteoarthritis that do show promise. However, the clinical extensibility, applicability, and interpretability of predictive tools should be considered during model development.

Disease progression and clinical outcomes in latent osteoarthritis phenotypes: Data from the Osteoarthritis Initiative

The prevalence of knee osteoarthritis (OA) is widespread and the heterogeneous patient factors and clinical symptoms in OA patients impede developing personalized treatments for OA patients. In this study, we used unsupervised and supervised machine learning to organize the heterogeneity in knee OA patients and predict disease progression in individuals from the Osteoarthritis Initiative (OAI) dataset. We identified four distinct knee OA phenotypes using unsupervised learning that were defined by nutrition, disability, stiffness, and pain (knee and back) and were strongly related to disease fate. Interestingly, the absence of supplemental vitamins from an individual's diet was protective from disease progression. Moreover, we established a phenotyping tool and prognostic model from 5 variables (WOMAC disability score of the right knee, WOMAC total score of the right knee, WOMAC total score of the left knee, supplemental vitamins and minerals frequency, and antioxidant combination multivitamins frequency) that can be utilized in clinical practice to determine the risk of knee OA progression in individual patients. We also developed a prognostic model to estimate the risk for total knee replacement and provide suggestions for modifiable variables to improve long-term knee health. This combination of unsupervised and supervised data-driven tools provides a framework to identify knee OA phenotype in a clinical scenario and personalize treatment strategies.

Disease progression and clinical outcomes in latent osteoarthritis phenotypes: Data from the Osteoarthritis Initiative

The prevalence of knee osteoarthritis (OA) is widespread and the heterogeneous patient factors and clinical symptoms in OA patients impede developing personalized treatments for OA patients. In this study, we used unsupervised and supervised machine learning to organize the heterogeneity in knee OA patients and predict disease progression in individuals from the Osteoarthritis Initiative (OAI) dataset. We identified four distinct knee OA phenotypes using unsupervised learning that were defined by nutrition, disability, stiffness, and pain (knee and back) and were strongly related to disease fate. Interestingly, the absence of supplemental vitamins from an individual's diet was protective from disease progression. Moreover, we established a phenotyping tool and prognostic model from 5 variables (WOMAC disability score of the right knee, WOMAC total score of the right knee, WOMAC total score of the left knee, supplemental vitamins and minerals frequency, and antioxidant combination multivitamins frequency) that can be utilized in clinical practice to determine the risk of knee OA progression in individual patients. We also developed a prognostic model to estimate the risk for total knee replacement and provide suggestions for modifiable variables to improve long-term knee health. This combination of unsupervised and supervised data-driven tools provides a framework to identify knee OA phenotype in a clinical scenario and personalize treatment strategies.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

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.

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

A joint multi-modal learning method for early-stage knee osteoarthritis disease classification

Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.

Region of interest-specific loss functions improve T2 quantification with ultrafast T2 mapping MRI sequences in knee, hip and lumbar spine

MRI T2 mapping sequences quantitatively assess tissue health and depict early degenerative changes in musculoskeletal (MSK) tissues like cartilage and intervertebral discs (IVDs) but require long acquisition times. In MSK imaging, small features in cartilage and IVDs are crucial for diagnoses and must be preserved when reconstructing accelerated data. To these ends, we propose region of interest-specific postprocessing of accelerated acquisitions: a recurrent UNet deep learning architecture that provides T2 maps in knee cartilage, hip cartilage, and lumbar spine IVDs from accelerated T2-prepared snapshot gradient-echo acquisitions, optimizing for cartilage and IVD performance with a multi-component loss function that most heavily penalizes errors in those regions. Quantification errors in knee and hip cartilage were under 10% and 9% from acceleration factors R = 2 through 10, respectively, with bias for both under 3 ms for most of R = 2 through 12. In IVDs, mean quantification errors were under 12% from R = 2 through 6. A Gray Level Co-Occurrence Matrix-based scheme showed knee and hip pipelines outperformed state-of-the-art models, retaining smooth textures for most R and sharper ones through moderate R. Our methodology yields robust T2 maps while offering new approaches for optimizing and evaluating reconstruction algorithms to facilitate better preservation of small, clinically relevant features.

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative

BACKGROUND

Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA).

PURPOSE

To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA.

STUDY TYPE

Retrospective study.

SUBJECTS

Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren-Lawrence [KL] grade: 0; final KL grade: 3 over the 10-year study).

FIELD STRENGTH/SEQUENCE

3.0-T and 14.1-T, biomechanics-based displacement-encoded imaging, fast spin echo, multi-slice multi-echo T2 mapping.

ASSESSMENT

We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades.

STATISTICAL TESTS

Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold at α = 0.05.

RESULTS

Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R²  = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not.

DATA CONCLUSION

Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early-stage degeneration.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Deep learning approach to predict pain progression in knee osteoarthritis

OBJECTIVE

To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA).

MATERIALS AND METHODS

The incidence and progression cohorts of the Osteoarthritis Initiative, a multi-center longitudinal study involving 9348 knees in 4674 subjects with or at risk of knee OA that began in 2004 and is ongoing, were used to conduct this retrospective analysis. A subset of knees without and with pain progression (defined as a 9-point or greater increase in pain score between baseline and two or more follow-up time points over the first 48 months) was randomly stratified into training (4200 knees with a mean age of 61.0 years and 60% female) and hold-out testing (500 knees with a mean age of 60.8 years and 60% female) datasets. A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors. A combined model was developed to combine demographic, clinical, and radiographic risk factors with DL analysis of baseline knee radiographs. Area under the curve (AUC) analysis was performed using the hold-out testing dataset to evaluate model performance.

RESULTS

The traditional model had an AUC of 0.692 (66.9% sensitivity and 64.1% specificity). The DL model had an AUC of 0.770 (76.7% sensitivity and 70.5% specificity), which was significantly higher (p < 0.001) than the traditional model. The combined model had an AUC of 0.807 (72.3% sensitivity and 80.9% specificity), which was significantly higher (p < 0.05) than the traditional and DL models.

CONCLUSIONS

DL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors.

Cutoff points of T1 rho/T2 mapping relaxation times distinguishing early-stage and advanced osteoarthritis

INTRODUCTION

The histopathology grading system is the gold standard post-operative method to evaluate cartilage degeneration in knee osteoarthritis (OA). Magnetic resonance imaging (MRI) T1 rho/T2 mapping imaging can be used for preoperative detection. An association between histopathology and T1 rho/T2 mapping relaxation times was suggested in previous research. However, the cutoff point was not determined among different histopathology grades. Our study aimed to determine the cutoff point of T1 rho/T2 mapping.

MATERIAL AND METHODS

T1 rho/T2 mapping images were acquired from 80 samples before total knee replacements. Then the histopathology grading system was applied.

RESULTS

The mean T1 rho/T2 mapping relaxation times of 80 samples were 39.17 ms and 37.98 ms respectively. Significant differences were found in T1 rho/T2 mapping values between early-stage and advanced OA (p < 0.001). The cutoff point for T1 rho was 33 ms with a sensitivity of 94.12 (95% CI: 80-99.3) and a specificity of 91.30 (95% CI: 79.2-97.6). The cutoff point for T2 mapping was suggested as 35.04 ms with a sensitivity of 88.24 (95% CI: 72.5-96.7) and specificity of 97.83 (95% CI: 88.5-99.9). After bootstrap simulation, the 95% CI of the T1 rho/T2 mapping cutoff point was estimated as 29.36 to 36.32 ms and 34.8 to 35.04 ms respectively. The area under the PR curve of T1 rho/T2 mapping was 0.972 (95% CI: 0.925-0.992) and 0.949 (95% CI: 0.877-0.989) respectively.

CONCLUSIONS

The cutoff point of T1 rho relaxation times, which was suggested as 33 ms, could be used to distinguish early-stage and advanced OA.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

OBJECTIVE

We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

DESIGN

We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison.

RESULTS

Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values.

CONCLUSIONS

Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.

Common Biochemical and Magnetic Resonance Imaging Biomarkers of Early Knee Osteoarthritis and of Exercise/Training in Athletes: A Narrative Review

Knee osteoarthritis (OA) is the most common joint disease of the world population. Although considered a disease of old age, OA also affects young individuals and, more specifically among them, those practicing knee-joint-loading sports. Predicting OA at an early stage is crucial but remains a challenge. Biomarkers that can predict early OA development will help in the design of specific therapeutic strategies for individuals and, for athletes, to avoid adverse outcomes due to exercising/training regimens. This review summarizes and compares the current knowledge of fluid and magnetic resonance imaging (MRI) biomarkers common to early knee OA and exercise/training in athletes. A variety of fluid biochemical markers have been proposed to detect knee OA at an early stage; however, few have shown similar behavior between the two studied groups. Moreover, in endurance athletes, they are often contingent on the sport involved. MRI has also demonstrated its ability for early detection of joint structural alterations in both groups. It is currently suggested that for optimal forecasting of early knee structural alterations, both fluid and MRI biomarkers should be analyzed as a panel and/or combined, rather than individually.

Visualizing pre-osteoarthritis: Integrating MRI UTE-T2* with mechanics and biology to combat osteoarthritis-The 2019 Elizabeth Winston Lanier Kappa Delta Award

Osteoarthritis (OA) is a leading cause of pain and disability for which disease-modifying treatments remain lacking. This is because the symptoms and radiographic changes of OA occur after the onset of likely irreversible changes. Defining and treating earlier disease states are therefore needed to delay or to halt OA progression. Taking this concept a step further, studying OA pathogenesis before disease onset by characterizing potentially reversible markers of increased OA risk to identify a state of "pre-osteoarthritis (pre-OA)" shifts the paradigm towards OA prevention. The purpose of this review is to summarize the 42 studies comprising the 2019 Kappa Delta Elizabeth Lanier Award where conceptualization of a systems-based definition for "pre-osteoarthritis (pre-OA)" was followed by demonstration of potentially reversible markers of heightened OA risk in patients after anterior cruciate ligament (ACL) injury and reconstruction. In the process, these efforts contributed a new magnetic resonance imaging method of ultrashort echo time (UTE) enhanced T2* mapping to visualize joint tissue damage before the development of irreversible changes. The studies presented here support a transformative approach to OA that accounts for interactions between mechanical, biological, and structural markers of OA risk to develop and evaluate new treatment strategies that can delay or prevent the onset of clinical disease. This body of work was inspired by and performed for patients. Shifting the paradigm from attempting to modify symptomatic radiographic OA towards monitoring and reversing markers of "pre-OA" opens the door for transforming the clinical approach to OA from palliation to prevention.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study

OBJECTIVE

To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class.

METHOD

T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier.

RESULTS

LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %.

CONCLUSION

LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Regenerative Potential of Mesenchymal Stem Cells for the Treatment of Knee Osteoarthritis and Chondral Defects: A Systematic Review and Meta-analysis

PURPOSE

To perform a systematic review and meta-analysis evaluating the effects of mesenchymal stem cells (MSCs) on cartilage regeneration and patient-reported pain and function.

METHODS

A systematic review was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using a PRISMA checklist. The Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, PubMed (2008-2019), EMBASE (2008-2019), and MEDLINE (2008-2019) were queried in July 2019 for literature reporting use of stem cells to treat knee osteoarthritis or chondral defects. Data describing administered treatment, subject population, injection type, duration of follow-up, pain and functional outcomes, and radiographic and magnetic resonance imaging findings were extracted. Risk of bias was assessed using the Downs and Black scale. Meta-analyses adjusted for random effects were performed, calculating pooled effect sizes in terms of patient-reported pain and function, cartilage quality, and cartilage volume.

RESULTS

Twenty-five studies with 439 subjects were identified. There was no significant difference in pain improvement between MSC treatment and controls (pooled standardized mean difference [SMD] = 0.23, P = .30). However, MSC treatment was significantly favored for functional improvement (SMD = 0.66, P < .001). There was improvement in cartilage volume after MSC treatment (SMD = 0.84, P < .001). Regarding cartilage quality, meta-analysis resulted in a small, nonsignificant effect size of 0.37 (95%, -0.03 to 0.77, P = .07). There was risk for potential bias among included studies, with 17 (68%) receiving either a grade of "poor" or "fair."

CONCLUSIONS

The pooled SMD from meta-analyses showed statistically significant effects of MSC on self-reported physical function but not self-reported pain. MSCs provided functional benefit only in patients who underwent concomitant surgery. However, this must be interpreted with caution, as there was substantial variability in MSC composition and mode of delivery. MSC treatment provided significant improvement in cartilage volume but not cartilage quality. Preliminary data regarding therapeutic properties of MSC treatment suggest significant heterogeneity in the current literature, and risk of bias is not negligible.

LEVEL OF EVIDENCE

II, Systematic Review and Meta-analysis.

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning

Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection at a reversible stage. We present an approach that enables sensitive OA detection in presymptomatic individuals. Our approach combines optimal mass transport theory with statistical pattern recognition. Eighty-six healthy individuals were selected from the Osteoarthritis Initiative, with no symptoms or visual signs of disease on imaging. On 3-y follow-up, a subset of these individuals had progressed to symptomatic OA. We trained a classifier to differentiate progressors and nonprogressors on baseline cartilage texture maps, which achieved a robust test accuracy of 78% in detecting future symptomatic OA progression 3 y prior to symptoms. This work demonstrates that OA detection may be possible at a potentially reversible stage. A key contribution of our work is direct visualization of the cartilage phenotype defining predictive ability as our technique is generative. We observe early biochemical patterns of fissuring in cartilage that define future onset of OA. In the future, coupling presymptomatic OA detection with emergent clinical therapies could modify the outcome of a disease that costs the United States healthcare system $16.5 billion annually. Furthermore, our technique is broadly applicable to earlier image-based detection of many diseases currently diagnosed at advanced stages today.

Risk factors for pain and functional impairment in people with knee and hip osteoarthritis: a systematic review and meta-analysis

OBJECTIVE

To identify risk factors for pain and functional deterioration in people with knee and hip osteoarthritis (OA) to form the basis of a future 'stratification tool' for OA development or progression.

DESIGN

Systematic review and meta-analysis.

METHODS

An electronic search of the literature databases, Medline, Embase, CINAHL, and Web of Science (1990-February 2020), was conducted. Studies that identified risk factors for pain and functional deterioration to knee and hip OA were included. Where data and study heterogeneity permitted, meta-analyses presenting mean difference (MD) and ORs with corresponding 95% CIs were undertaken. Where this was not possible, a narrative analysis was undertaken. The Downs & Black tool assessed methodological quality of selected studies before data extraction. Pooled analysis outcomes were assessed and reported using the Grading of Reccomendation, Assessment, Development and Evaluation (GRADE) approach.

RESULTS

82 studies (41 810 participants) were included. On meta-analysis: there was moderate quality evidence that knee OA pain was associated with factors including: Kellgren and Lawrence≥2 (MD: 2.04, 95% CI 1.48 to 2.81; p<0.01), increasing age (MD: 1.46, 95% CI 0.26 to 2.66; p=0.02) and whole-organ MRI scoring method (WORMS) knee effusion score ≥1 (OR: 1.35, 95% CI 0.99 to 1.83; p=0.05). On narrative analysis: knee OA pain was associated with factors including WORMS meniscal damage ≥1 (OR: 1.83). Predictors of joint pain in hip OA were large acetabular bone marrow lesions (BML; OR: 5.23), chronic widespread pain (OR: 5.02) and large hip BMLs (OR: 4.43).

CONCLUSIONS

Our study identified risk factors for clinical pain in OA by imaging measures that can assist in predicting and stratifying people with knee/hip OA. A 'stratification tool' combining verified risk factors that we have identified would allow selective stratification based on pain and structural outcomes in OA.

PROSPERO REGISTRATION NUMBER

CRD42018117643.

Artificial Intelligence and Orthopaedics: An Introduction for Clinicians

➤Artificial intelligence (AI) provides machines with the ability to perform tasks using algorithms governed by pattern recognition and self-correction on large amounts of data to narrow options in order to avoid errors. ➤The 4 things necessary for AI in medicine include big data sets, powerful computers, cloud computing, and open source algorithmic development. ➤The use of AI in health care continues to expand, and its impact on orthopaedic surgery can already be found in diverse areas such as image recognition, risk prediction, patient-specific payment models, and clinical decision-making. ➤Just as the business of medicine was once considered outside the domain of the orthopaedic surgeon, emerging technologies such as AI warrant ownership, leverage, and application by the orthopaedic surgeon to improve the care that we provide to the patients we serve. ➤AI could provide solutions to factors contributing to physician burnout and medical mistakes. However, challenges regarding the ethical deployment, regulation, and the clinical superiority of AI over traditional statistics and decision-making remain to be resolved.

Histological Grade and Magnetic Resonance Imaging Quantitative T1rho/T2 Mapping in Osteoarthritis of the Knee: A Study in 20 Patients

Background

Magnetic resonance imaging (MRI) of osteoarthritis (OA) of the knee is a preoperative method of joint assessment. Histology of the joint is invasive and performed after surgery. T1rho/T2 MRI mapping is a new preoperative method of quantifying joint changes. This study aimed to analyze and compare the histological changes in the joint cartilage with the use of quantitative T1rho/T2 MRI mapping in patients with OA of the knee.

Material/Methods

Twenty patients with OA of the knee (20 knees) underwent preoperative MRI with T1rho mapping, T2 mapping, T1-weighted, and T2-weighted fat-suppressed MRI sequences. The degree of OA of the knee on MRI was graded according to the Osteoarthritis Research Society International (OARSI) criteria and the Kellgren-Lawrence grading system. Histological grading of OA used the OARSI criteria. Four tibiofemoral condyles were assessed histologically, and the degree of cartilage destruction was determined using the OARSI criteria. Two investigators performed cartilage segmentation for T1rho/T2 values.

Results

Histology of the four knee joint condyles confirmed mild to severe OA. The histology of the cartilage thickness (P<0.001) and the MRI findings of the distal medial condyle (P<0.00) were significantly different from the other three knee joint condyles. The T2 and T1rho values of each condyle were significantly correlated with the histological grade (II–IV) of the joint condyles, including the cartilage volume, cartilage defects, thickness, and bone lesions (P<0.05).

Conclusions

In 20 patients with OA of the knee, preoperative T2/T1rho MRI identified Grade II–IV OA changes in the joint.

Quantitative T2 MRI Mapping and 12-Month Follow-up in a Randomized, Blinded, Placebo Controlled Trial of Bone Marrow Aspiration and Concentration for Osteoarthritis of the Knees

OBJECTIVE

Bone marrow aspiration and concentration (BMAC) is becoming a more common regenerative therapy for musculoskeletal pathology. In our current pilot study, we studied patients with mild-to-moderate bilateral knee osteoarthritis, compared pain at 12-month follow-up between BMAC-injected and saline-injected knees, and examined cartilage appearance measured by magnetic resonance imaging (MRI) T2 quantitative mapping.

DESIGN

Twenty-five patients with mild-to-moderate bilateral osteoarthritic knee pain were randomized to receive BMAC into one knee and saline placebo into the other. Bone marrow was aspirated from the iliac crests, concentrated in an automated centrifuge, combined with platelet-poor plasma for knee injection, and compared with saline injection into the contralateral knee. Primary outcome measures were T2 MRI cartilage mapping at 6-month and Visual Analog Scale and Osteoarthritis Research Society International Intermittent and Constant Osteoarthritis Pain scores and radiographs at 12-month follow-up.

RESULTS

Constant, intermittent, and overall knee pain remained significantly decreased from baseline at 12-month follow-up (all P ⩽ 0.01), with no apparent difference between BMAC- and saline-treated knees (all P ⩾ 0.54). A similar significant increase from baseline to 12-month follow-up regarding quality of life was observed for both BMAC- and saline-treated knees (all P ⩽ 0.04). T2 quantitative MRI mapping showed no significant changes as a result of treatment.

CONCLUSIONS

BMAC is safe to perform and relieves pain from knee arthritis but showed no superiority to saline injection at 12-month follow-up. MRI cartilage sequences failed to show regenerative benefit with single BMAC injection. The mechanisms of action that led to pain relief remain unclear and warrant further studies.

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.

RESULTS

In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.

CONCLUSION

Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

Knee pain as a predictor of structural progression over 4 years: data from the Osteoarthritis Initiative, a prospective cohort study

Background

There is evidence that knee pain not only is a consequence of structural deterioration in osteoarthritis (OA) but also contributes to structural progression. Clarifying this is important because targeting the factors related to knee pain may offer a clinical approach for slowing the progression of knee OA. The aim of this study was to examine whether knee pain over 1 year predicted cartilage volume loss, incidence and progression of radiographic osteoarthritis (ROA) over 4 years.

Methods

Osteoarthritis Initiative participants with no ROA (Kellgren-Lawrence grade ≤ 1) (n = 2120) and with ROA (Kellgren-Lawrence grade > 2) (n = 2249) were examined. Knee pain was assessed at baseline and 1 year using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Knee pain patterns were categorised as no pain (WOMAC pain < 5 at baseline and 1 year), fluctuating pain (WOMAC pain > 5 at either time point) and persistent pain (WOMAC pain > 5 at both time points). Cartilage volume, incidence and progression of ROA were assessed using magnetic resonance imaging and x-rays at baseline and 4-years.

Results

In both non-ROA and ROA, greater baseline WOMAC knee pain score was associated with increased medial and lateral cartilage volume loss (p ≤ 0.001), incidence (OR 1.07, 95% CI 1.01–1.13) and progression (OR 1.07, 95% CI 1.03–1.10) of ROA. Non-ROA and ROA participants with fluctuating and persistent knee pain had increased cartilage volume loss compared with those with no pain (p for trend ≤ 0.01). Non-ROA participants with fluctuating knee pain had increased risk of incident ROA (OR 1.62, 95% CI 1.04–2.54), corresponding to a number needed to harm of 19.5. In ROA the risk of progressive ROA increased in participants with persistent knee pain (OR 1.82, 95% CI 1.28–2.60), corresponding to a number needed to harm of 9.6.

Conclusions

Knee pain over 1 year predicted accelerated cartilage volume loss and increased risk of incident and progressive ROA. Early management of knee pain and controlling knee pain over time by targeting the underlying mechanisms may be important for preserving knee structure and reducing the burden of knee OA.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1751-4) contains supplementary material, which is available to authorized users.

Quantitative mapping of glenohumeral cartilage in asymptomatic subjects using 3 T magnetic resonance imaging

OBJECTIVE

The purpose of this study was to develop quantitative T2 mapping methodology in asymptomatic shoulders for the entire mappable region of the glenohumeral cartilage in the coronal and sagittal planes, to assess the feasibility and limitations of the development of a diagnostic tool for future application in symptomatic patients.

MATERIALS AND METHODS

Twenty-one asymptomatic volunteers underwent sagittal and coronal glenohumeral T2 mapping, as the spherical geometry of the humeral head obviates the need to evaluate the entire glenohumeral cartilage in a single plane. The humeral head cartilage orthogonal to the mapping plane was manually segmented in the sagittal and coronal planes, whereas the glenoid cartilage was segmented in the coronal plane. Cartilage T2 summary statistics were calculated and coverage in each mapping plane was qualitatively assessed.

RESULTS

The mean ± standard deviation of the glenoid cartilage T2 was 38 ± 2 ms. The coronal and sagittal mapping planes captured different regions of the humeral head with some overlap: inferior-medial to superior-lateral versus superior/superior-lateral to anterior-lateral and posterior-lateral respectively. The mean humeral head cartilage T2 in the coronal plane was 41 ± 3 ms, which was significantly different (p < 0.05) from the sagittal plane mean of 34 ± 2 ms.

CONCLUSION

This study measured characteristic glenoid and humeral head cartilage T2 values over the area mappable with two planes. Importantly, this study demonstrated that two-dimensional mapping in a single plane or two combined planes cannot capture the entirety of the semi-spherical humeral head cartilage. This highlights the need for three-dimensional T2 mapping techniques in the shoulder.

MRI UTE-T2* profile characteristics correlate to walking mechanics and patient reported outcomes 2 years after ACL reconstruction

OBJECTIVE

Quantitative magnetic resonance imaging (MRI) ultrashort echo time (UTE) T2* is sensitive to cartilage deep tissue matrix changes after anterior cruciate ligament reconstruction (ACLR). This study was performed to determine whether UTE-T2* profile analysis is a useful clinical metric for assessing cartilage matrix degeneration. This work tests the hypotheses that UTE-T2* depthwise rates of change (profile slopes) correlate with clinical outcome metrics of walking mechanics and patient reported outcomes (PRO) in patients 2 years after ACLR.

DESIGN

Thirty-six patients 2 years after ACLR completed knee MRI, gait analysis, and PRO. UTE-T2* maps were generated from MRI images and depthwise UTE-T2* profiles were calculated for weight-bearing cartilage in the medial compartment. UTE-T2* profiles from 14 uninjured subjects provided reference values. UTE-T2* profile characteristics, including several different measures of profile slope, were tested for correlation to kinetic and kinematic measures of gait and also to PRO.

RESULTS

Decreasing UTE-T2* profile slopes in ACLR knees moderately correlated with increasing knee adduction moments (r = 0.41, P < 0.015), greater external tibial rotation (r = 0.44, P = 0.007), and moderately negatively correlated with PRO (r = -0.36, P = 0.032). UTE-T2* profiles from both ACLR and contralateral knees of ACLR subjects differed from that of uninjured controls (P < 0.015).

CONCLUSIONS

The results of this study suggest that decreasing UTE-T2* profile slopes reflect cartilage deep tissue collagen matrix disruption in a population at increased risk for knee osteoarthritis (OA). That UTE-T2* profiles were associated with mechanical and patient reported measures of clinical outcomes support further study into a potential mechanistic relationship between these factors and OA development.

Magnetic Resonance Imaging for Patellofemoral Chondromalacia: Is There a Role for T2 Mapping?

Background:

Patellofemoral pain is common, and treatment is guided by the presence and grade of chondromalacia.

Purpose:

To evaluate and compare the sensitivity and specificity in detecting and grading chondral abnormalities of the patella between proton density fat suppression (PDFS) and T2 mapping magnetic resonance imaging (MRI).

Study Design:

Cohort study; Level of evidence, 2.

Methods:

A total of 25 patients who underwent MRI of the knee with both a PDFS sequence and T2 mapping and subsequently underwent arthroscopic knee surgery were included. The cartilage surface of the patella was graded on both MRI sequences by 2 independent, blinded radiologists. Cartilage was then graded during arthroscopic surgery by a sports medicine fellowship–trained orthopaedic surgeon. Reliability, sensitivity, specificity, and accuracy were determined for both MRI methods. The findings during arthroscopic surgery were considered the gold standard.

Results:

Intraobserver and interobserver agreement for both PDFS (98.5% and 89.4%, respectively) and T2 mapping (99.4% and 91.3%, respectively) MRI were excellent. For T2 mapping, the sensitivity (61%) and specificity (64%) were comparable, whereas for PDFS there was a lower sensitivity (37%) but higher specificity (81%) in identifying cartilage abnormalities. This resulted in a similar accuracy for PDFS (59%) and T2 mapping (62%).

Conclusion:

Both PDFS and T2 mapping MRI were reliable but only moderately accurate in predicting patellar chondromalacia found during knee arthroscopic surgery.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi-slice T2 -weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for "progression to symptomatic OA" using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy.

CLINICAL SIGNIFICANCE

Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243-2250, 2017.

How should worsening in osteoarthritis be defined? Development and initial validation of preliminary criteria for clinical worsening in knee and hip osteoarthritis

OBJECTIVES

There is a need to define and validate measures of clinical worsening in knee and hip osteoarthritis (OA). The objectives of this exploratory project were: (i) to characterize worsening criteria in knee and hip OA using psychometric methods; (ii) to estimate their sensitivity and specificity; and (iii) to validate and compare these criteria with worsening criteria previously described in the literature.

METHOD

An Expert Group reached consensus on 10 sets of worsening criteria to be tested in observational data sets of patients with knee or hip OA who received multimodal conservative treatment. These sets included 219 patients (derivation cohort) and 296 patients (validation cohort). We estimated minimal clinically important worsening (MCIW) values for pain, function, stiffness, and patient global assessment, and tested candidate worsening criteria in the derivation cohort. Finally, using patient judgement, we examined the sensitivity and specificity of literature-based as well as candidate worsening criteria in the validation cohort.

RESULTS

Literature-based worsening criteria were found to have high specificity (range 60-92%) but low sensitivity (range 22-59%). Two out of 10 candidate worsening criteria constructed by the Expert Group showed an acceptable combination of sensitivity and specificity in the derivation cohort, which was confirmed in the validation cohort (ranging from 54% to 65% and 67% to 74%, respectively).

CONCLUSIONS

This is the first study to describe symptomatic worsening criteria based on expert consensus after examining the performance of candidate criteria derived from the literature applied to data in an observational study. The newly proposed worsening criteria show an acceptable combination of sensitivity and specificity.

Baseline Cartilage Thickness and Meniscus Extrusion Predict Longitudinal Cartilage Loss by Quantitative Magnetic Resonance Imaging: Data From the Osteoarthritis Initiative

OBJECTIVE

This study aimed to assess how meniscus damage and baseline cartilage thickness influence the rate of cartilage loss and knee pain.

METHODS

Of 4796 participants in the Osteoarthritis Initiative, 86 had baseline and 48-month follow-up quantitative magnetic resonance imaging data for medial compartment cartilage thickness. Baseline meniscus pathology was scored by a musculoskeletal radiologist using Whole-Organ Magnetic Resonance Imaging Score. Findings were correlated with 72-month Knee injury and Osteoarthritis Outcome Score.

RESULTS

Univariate analysis showed cartilage change was not influenced by demographic variables. Multivariable regression revealed that initial cartilage thickness (-1.07 mm at 48 months for every 1 mm decrease at baseline, P < 0.001) and meniscus extrusion (-0.33 mm if present at baseline, P < 0.001) were the strongest predictors of medial compartment cartilage thickness at 48 months. Knee injury and Osteoarthritis Outcome Score pain scores did not correlate with cartilage loss.

CONCLUSIONS

Baseline cartilage thickness and meniscus extrusion are important and independent predictors for accelerated cartilage loss. However, the degree of cartilage loss did not correlate with midterm change in clinical outcome scores.

Layer-specific femorotibial cartilage T2 relaxation time in knees with and without early knee osteoarthritis: Data from the Osteoarthritis Initiative (OAI)

Magnetic resonance imaging (MRI)-based spin-spin relaxation time (T2) mapping has been shown to be associated with cartilage matrix composition (hydration, collagen content &orientation). To determine the impact of early radiographic knee osteoarthritis (ROA) and ROA risk factors on femorotibial cartilage composition, we studied baseline values and one-year change in superficial and deep cartilage T2 layers in 60 subjects (age 60.6 ± 9.6 y; BMI 27.8 ± 4.8) with definite osteophytes in one knee (earlyROA, n = 32) and with ROA risk factors in the contralateral knee (riskROA, n = 28), and 89 healthy subjects (age 55.0 ± 7.5 y; BMI 24.4 ± 3.1) without signs or risk factors of ROA. Baseline T2 did not differ significantly between earlyROA and riskROA knees in the superficial (48.0 ± 3.5 ms vs. 48.1 ± 3.1 ms) or the deep layer (37.3 ± 2.5 ms vs. 37.3 ± 1.8 ms). However, healthy knees showed significantly lower superficial layer T2 (45.4 ± 2.3 ms) than earlyROA or riskROA knees (p ≤ 0.001) and significantly lower deep layer T2 (35.8 ± 1.8 ms) than riskROA knees (p = 0.006). Significant longitudinal change in T2 (superficial: 0.5 ± 1.4 ms; deep: 0.8 ± 1.3 ms) was only detected in healthy knees. These results do not suggest an association of early ROA (osteophytes) with cartilage composition, as assessed by T2 mapping, whereas cartilage composition was observed to differ between knees with and without ROA risk factors.

T1ρ and T2 relaxation times are associated with progression of hip osteoarthritis

OBJECTIVE

To evaluate whether baseline T1ρ and T2 relaxation times of hip cartilage are associated with magnetic resonance imaging (MRI) based progression of hip osteoarthritis (OA) at 18 months.

METHODS

3T MRI studies of the hip were obtained at baseline and 18-month follow-up for 54 subjects without evidence of severe OA at baseline [Kellgren-Lawrence (KL) score of 0-3]. 2D fast spin-echo sequences were used for semi-quantitative morphological scoring of cartilage lesions and a combined T1ρ/T2 sequence was used to quantitatively assess cartilage composition. Progression of hip OA was defined based on incident or progression of morphological semi-quantitative grade at 18 months. Baseline T1ρ and T2 relaxation times were compared between progressors and non-progressors using one-way analysis of variance and Mann-Whitney U tests and used to predict progression with binary logistic regression after adjusting for age, gender, body mass index, and KL score. Additionally, a novel voxel-based relaxometry technique was used to compare the spatial distribution of baseline T1ρ and T2 between progressors and non-progressors.

RESULTS

Significantly higher baseline T1ρ and T2 values were observed in hip OA progressors compared to non-progressors, particularly in the posterosuperior and anterior aspects of the femoral cartilage. Logistic regression showed that higher baseline T1ρ or T2 values in the femoral cartilage were significantly associated with progression of femoral cartilage lesions at 18 months.

CONCLUSION

T1ρ and T2 relaxation parameters are associated with morphological cartilage degeneration at 18 months and may serve as potential imaging biomarkers for progression of cartilage lesions in hip OA.

Patellofemoral instability in children: T2 relaxation times of the patellar cartilage in patients with and without patellofemoral instability and correlation with morphological grading of cartilage damage

BACKGROUND

Patellofemoral instability is one of the most common causes of cartilage damage in teenagers.

OBJECTIVE

To quantitatively evaluate the patellar cartilage in patients with patellofemoral instability using T2 relaxation time maps (T2 maps), compare the values to those in patients without patellofemoral instability and correlate them with morphological grades in patients with patellofemoral instability.

MATERIALS AND METHODS

Fifty-three patients with patellofemoral instability (mean age: 15.9 ± 2.4 years) and 53 age- and gender-matched patients without patellofemoral instability were included. Knee MR with axial T2 map was performed. Mean T2 relaxation times were obtained at the medial, central and lateral zones of the patellar cartilage and compared between the two groups. In the patellofemoral instability group, morphological grading of the patellar cartilage (0-4) was performed and correlated with T2 relaxation times.

RESULTS

Mean T2 relaxation times were significantly longer in the group with patellofemoral instability as compared to those of the control group across the patellar cartilage (Student's t-test, P<0.05) with the longest time at the central area. Positive correlation was seen between mean T2 relaxation time and morphological grading (Pearson correlation coefficiency, P<0.001). T2 increased with severity of morphological grading from 0 to 3 (mixed model, P<0.001), but no statistical difference was seen between grades 3 and 4.

CONCLUSION

In patellofemoral instability, patellar cartilage damage occurs across the entire cartilage with the highest T2 values at the apex. T2 relaxation times directly reflect the severity in low-grade cartilage damage, which implies an important role for T2 maps in differentiating between normal and low-grade cartilage damage.

T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

OBJECTIVE

The aim of this work is to use quantitative magnetic resonance imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps might predict symptomatic OA progression.

METHODS

Patients were selected from the Osteoarthritis Initiative (OAI), a prospective cohort. Two groups were identified: a symptomatic OA progression group and a control group. At baseline, both groups were asymptomatic (Western Ontario and McMaster Universities Arthritis [WOMAC] pain score total <10) with no radiographic evidence of OA (Kellgren-Lawrence [KL] score ≤ 1). The OA progression group (n = 103) had a change in total WOMAC score greater than 10 by the 3-year follow-up. The control group (n = 79) remained asymptomatic, with a change in total WOMAC score less than 10 at the 3-year follow-up. A classifier was designed to predict OA progression in an independent population based on T2 map cartilage signal variation. The classifier was designed using a nearest neighbor classification based on a Gaussian Mixture Model log-likelihood fit of T2 map cartilage voxel intensities.

RESULTS

The use of T2 map signal variation to predict symptomatic OA progression in asymptomatic individuals achieved a specificity of 89.3 %, a sensitivity of 77.2 %, and an overall accuracy rate of 84.2 %.

CONCLUSION

T2 map signal variation can predict symptomatic knee OA progression in asymptomatic individuals, serving as a possible early OA imaging biomarker.

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

OBJECTIVE

The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.

METHOD

An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.

RESULTS

The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.

CONCLUSION

MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

T2 Relaxation time quantitation differs between pulse sequences in articular cartilage

BACKGROUND

To compare T2 relaxation time measurements between MR pulse sequences at 3 Tesla in agar phantoms and in vivo patellar, femoral, and tibial articular cartilage.

METHODS

T2 relaxation times were quantified in phantoms and knee articular cartilage of eight healthy individuals using a single echo spin echo (SE) as a reference standard and five other pulse sequences: multi-echo SE (MESE), fast SE (2D-FSE), magnetization-prepared spoiled gradient echo (3D-MAPSS), three-dimensional (3D) 3D-FSE with variable refocusing flip angle schedules (3D vfl-FSE), and quantitative double echo steady state (qDESS). Cartilage was manually segmented and average regional T2 relaxation times were obtained for each sequence. A regression analysis was carried out between each sequence and the reference standard, and root-mean-square error (RMSE) was calculated.

RESULTS

Phantom measurements from all sequences demonstrated strong fits (R(2)  > 0.8; P < 0.05). For in vivo cartilage measurements, R(2) values, slope, and RMSE were: MESE: 0.25/0.42/5.0 ms, 2D-FSE: 0.64/1.31/9.3 ms, 3D-MAPSS: 0.51/0.66/3.8 ms, 3D vfl-FSE: 0.30/0.414.2 ms, qDESS: 0.60/0.90/4.6 ms.

CONCLUSION

2D-FSE, qDESS, and 3D-MAPSS demonstrated the best fits with SE measurements as well as the greatest dynamic ranges. The 3D-MAPSS, 3D vfl-FSE, and qDESS demonstrated the closest average measurements to SE. Discrepancies in T2 relaxation time quantitation between sequences suggest that care should be taken when comparing results between studies.

Validation of CBCT for the computation of textural biomarkers

Osteoarthritis (OA) is associated with significant pain and 42.6% of patients with TMJ disorders present with evidence of TMJ OA. However, OA diagnosis and treatment remain controversial, since there are no clear symptoms of the disease. The subchondral bone in the TMJ is believed to play a major role in the progression of OA. We hypothesize that the textural imaging biomarkers computed in high resolution Conebeam CT (hr-CBCT) and μCT scans are comparable. The purpose of this study is to test the feasibility of computing textural imaging biomarkers in-vivo using hr-CBCT, compared to those computed in μCT scans as our Gold Standard. Specimens of condylar bones obtained from condylectomies were scanned using μCT and hr-CBCT. Nine different textural imaging biomarkers (four co-occurrence features and five run-length features) from each pair of μCT and hr-CBCT were computed and compared. Pearson correlation coefficients were computed to compare textural biomarkers values of μCT and hr-CBCT. Four of the nine computed textural biomarkers showed a strong positive correlation between biomarkers computed in μCT and hr-CBCT. Higher correlations in Energy and Contrast, and in GLN (grey-level non-uniformity) and RLN (run length non-uniformity) indicate quantitative texture features can be computed reliably in hr-CBCT, when compared with μCT. The textural imaging biomarkers computed in-vivo hr-CBCT have captured the structure, patterns, contrast between neighboring regions and uniformity of healthy and/or pathologic subchondral bone. The ability to quantify bone texture non-invasively now makes it possible to evaluate the progression of subchondral bone alterations, in TMJ OA.

Osteoarthritis year in review 2014: imaging

PURPOSE

This narrative review covers original publications related to imaging in osteoarthritis (OA) published in English between April 2013 and March 2014. In vitro data, animal studies and studies with less than 20 observations were not included.

METHODS

To extract relevant studies, an extensive PubMed database search was performed based on, but not limited to the query terms "Osteoarthritis" in combination with "MRI", "Imaging", "Radiography", "Ultrasound", "Computed Tomography" and "Nuclear Medicine". Publications were sorted according to relevance based on potential impact to the OA research community with the overarching goal of a balanced overview covering all aspects of imaging. Focus was on publications in high impact special interest journals. The literature will be presented in a methodological fashion covering radiography, ultrasound, compositional and morphologic Magnetic resonance imaging (MRI), and from an anatomic perspective including bone, muscle, meniscus and synovitis.

RESULTS AND CONCLUSIONS

Imaging research in OA in the last year was characterized by a strong focus on MRI-based studies dealing with epidemiological and methodological aspects of the disease. Ultrastructural tissue assessment specifically of cartilage and meniscus using compositional MRI is evolving further. Additional subsets of the large publicly available Osteoarthritis Initiative (OAI) MRI dataset are being analyzed at present and have been published with muscle analyses coming increasingly into the focus of the community. Bone parameters were evaluated using varying technology and a persistent interest in inflammatory disease manifestations has been noted. Other modalities than MRI have been less explored. To date most OA imaging research is still focused on the knee joint.

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

The Osteoarthritis Initiative (OAI) is a multicentre, prospective, observational, cohort study of knee osteoarthritis (OA) that began recruitment in 2004. The OAI provides public access to clinical and image data, enabling researchers to examine risk factors/predictors and the natural history of knee OA incidence and progression, and the qualification of imaging and other biomarkers. In this narrative review, we report imaging findings and lessons learned 10 years after enrolment has started. A literature search for full text articles published from the OAI was performed up to 31 December 2013 using Pubmed and the OAI web page. We summarise the rationale, design and imaging protocol of the OAI, and the history of OAI publications. We review studies from early partial, and later full OAI public data releases. The latter are structured by imaging method and tissue, reviewing radiography and then MRI findings on cartilage morphology, cartilage lesions and composition (T2), bone, meniscus, muscle and adipose tissue. Finally, analyses directly comparing findings from MRI and radiography are summarised. Ten years after the first participants were enrolled and first papers published, the OAI has become an invaluable resource to the OA research community. It has fuelled novel methodological approaches of analysing images, and has provided a wealth of information on OA pathophysiology. Continued collection and public release of long-term observations will help imaging measures to gain scientific and regulatory acceptance as 'prognostic' or 'efficacy of intervention' biomarkers, potentially enabling shorter and more efficient clinical trials that can test structure-modifying therapeutic interventions (NCT00080171).

Early T2 changes predict onset of radiographic knee osteoarthritis: data from the osteoarthritis initiative

OBJECTIVE

To evaluate whether T2 relaxation time measurements obtained at 3 T MRI predict the onset of radiographic knee osteoarthritis (OA).

MATERIALS AND METHODS

We performed a nested case-control study of incident radiographic knee OA in the Osteoarthritis Initiative cohort. Cases were 50 knees with baseline Kellgren-Lawrence (KL) grade of 0 that developed KL grade of 2 or more over a 4-year period. Controls were 80 knees with KL grade of 0 after 4 years of follow-up. Baseline T2 relaxation time measurements and laminar analysis of T2 in deep and superficial layers were performed in all knee compartments. The association of T2 values with incident OA was assessed with logistic regression and differences in T2 values by case-control status with linear regression, adjusting for age, sex, body mass index (BMI) and other covariates.

RESULTS

Baseline T2 values in all compartments except the medial tibia were significantly higher in knees that developed OA compared with controls and were particularly elevated in the superficial cartilage layers in all compartments. There was an increased likelihood of incident knee OA associated with higher baseline T2 values, particularly in the patella, adjusted OR per 1 SD increase in T2 (3.37 (95% CI 1.72 to 6.62)), but also in the medial femur (1.90 (1.07 to 3.39)), lateral femur (2.17 (1.11 to 4.25)) and lateral tibia (2.23 (1.16 to 4.31)).

CONCLUSIONS

These findings suggest that T2 values assessed when radiographic changes are not yet apparent may be useful in predicting the development of radiological tibiofemoral OA.

Longitudinal evaluation of T1ρ and T2 spatial distribution in osteoarthritic and healthy medial knee cartilage

OBJECTIVE

To investigate longitudinal changes in laminar and spatial distribution of knee articular cartilage magnetic resonance imaging (MRI) T1ρ and T2 relaxation times, in individuals with and without medial compartment cartilage defects.

DESIGN

All subjects (at baseline n = 88, >18 years old) underwent 3-Tesla knee MRI at baseline and annually thereafter for 3 years. The MR studies were evaluated for presence of cartilage defects (modified Whole-Organ Magnetic Resonance Imaging Scoring - mWORMS), and quantitative T1ρ and T2 relaxation time maps. Subjects were segregated into those with (mWORMS ≥2) and without (mWORMS ≤1) cartilage lesions at the medial tibia (MT) or medial femur (MF) at each time point. Laminar (bone and articular layer) and spatial (gray level co-occurrence matrix - GLCM) distribution of the T1ρ and T2 relaxation time maps were calculated. Linear regression models (cross-sectional) and Generalized Estimating Equations (GEEs) (longitudinal) were used.

RESULTS

Global T1ρ, global T2 and articular layer T2 relaxation times at the MF, and global and articular layer T2 relaxation times at the MT, were higher in subjects with cartilage lesions compared to those without lesions. At the MT global T1ρ relaxation times were higher at each time point in subjects with lesions. MT T1ρ and T2 became progressively more heterogeneous than control compartments over the course of the study.

CONCLUSION

Spatial distribution of T1ρ and T2 relaxation time maps in medial knee OA using GLCM technique may be a sensitive indicator of cartilage deterioration, in addition to whole-compartment relaxation time data.