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

Comparison of MRI and arthroscopy findings for transitional zone cartilage damage in the acetabulum of the hip joint

OBJECTIVE

To assess the performance of morphologic and hypointense signal changes on MRI to predict grades and types of acetabular cartilage damage in the chondrolabral transitional zone (TZ) of the hip identified at arthroscopy.

MATERIALS AND METHODS

This retrospective single-center study reviewed conventional 3T MRI hip studies from individuals with symptomatic femoroacetabular impingement (FAI) and subsequent hip arthroscopy surgery within 6 months. Independent review was made by three radiologists for the presence of morphologic damage or a hypointense signal lesion in the TZ on MRI. Fleiss' kappa statistic was used to assess inter-reader agreement. The degree of TZ surfacing damage (modified Outerbridge grades 1-4) and presence of non-surfacing wave sign at arthroscopic surgery were collected. Relationship between sensitivity and lesion grade was examined.

RESULTS

One hundred thirty-six MRI hip studies from 40 males and 74 females were included (mean age 28.5 years, age range 13-54 years). MRI morphologic lesions had a sensitivity of 64.9-71.6% and specificity of 48.4-67.7% for arthroscopic surfacing lesions, with greater sensitivity seen for higher grade lesions. Low sensitivity was seen for wave sign lesions (34.5-51.7%). MRI hypointense signal lesions had a sensitivity of 26.3-62% and specificity of 43.8-78.0% for any lesion. Inter-reader agreement was moderate for morphologic lesions (k = 0.601) and poor for hypointense signal lesions (k = 0.097).

CONCLUSION

Morphologic cartilage damage in the TZ on MRI had moderate sensitivity for any cartilage lesion, better sensitivity for higher grade lesions, and poor sensitivity for wave sign lesions. The diagnostic value of hypointense signal lesions was uncertain.

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.

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.

Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions

Background

Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify opportunities for future model development.

Methods

We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression prediction model.

Results

We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set.

Conclusion

Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA prediction models.

Value of 3.0T MRI T2 mapping combined with SWI for the assessment of early lesions in hemophilic arthropathy

PURPOSE

To explore the value of magnetic resonance imaging (MRI) T2 mapping combined with susceptibility-weighted imaging (SWI) in detecting early cartilage damage and joint bleeding in the hemophilic arthropathy (HA).

METHODS

147 patients and 56 healthy controls were prospectively recruited. The knees were divided into groups A and B according to the criteria of the International Cartilage Repair Society (ICRS). The Regions of Interest (ROIs) of T2 mapping were drawn for the patella, lateral and medial femoral condyle, and lateral and medial tibial condyle. The T2 values were compared between the patients and control group using one-way ANOVA. The joint count data of International Prophylaxis Study Group (IPSG) scores of conventional and SWI sequences were statistically described using the composition ratio, and the rank sum test was used for the difference analysis.

RESULTS

Finally, there were 99 joints in the control group, 135 knees in group A, and 94 knees in group B. There was a significant difference between the T2 value in each subgroup. Comparison of T2 value groups in each cartilage partition, except for group A and group B of the patella, revealed significant differences (all P<0.05). SWI was likely more sensitive than conventional sequences in detecting hemosiderin deposits in hemophilic joints. In addition, the IPSG scores detected by the SWI were generally higher than those of conventional sequences.

CONCLUSIONS

MR T2 mapping combined with SWI has great potential to be used for detecting early cartilage damage and micro-hemosiderin deposition in hemophiliac arthropathies and developing preventative treatment plans.

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.

Review of Quantitative Knee Articular Cartilage MR Imaging

Osteoarthritis (OA) is one of the most prevalent disorders in today’s society, resulting in significant socio-economic costs and morbidity. MRI is widely used as a non-invasive imaging tool for OA of the knee. However, conventional knee MRI has limitations to detect subtle early cartilage degeneration before morphological changes are visually apparent. Novel MRI pulse sequences for cartilage assessment have recently received increased attention due to newly developed compositional MRI techniques, including: T2 mapping, T1rho mapping, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), sodium MRI, diffusion-weighted imaging (DWI)/ diffusion tensor imaging (DTI), ultrashort TE (uTE), and glycosaminoglycan specific chemical exchange saturation transfer (gagCEST) imaging. In this article, we will first review these quantitative assessments. Then, we will discuss the variations of quantitative values of knee articular cartilage with cartilage layer (depth)- and angle (regional)-dependent approaches. Multiple MRI sequence techniques can discern qualitative differences in knee cartilage. Normal articular hyaline cartilage has a zonal variation in T2 relaxation times with increasing T2 values from the subchondral bone to the articular surface. T1rho values were also higher in the superficial layer than in the deep layer in most locations in the medial and lateral femoral condyles, including the weight-bearing portion. Magic angle effect on T2 mapping is clearly observed in the both medial and lateral femoral condyles, especially within the deep layers. One of the limitations for clinical use of these compositional assessments is a long scan time. Recent new approaches with compressed sensing (CS) and MR fingerprinting (MRF) have potential to provide accurate and fast quantitative cartilage assessments.

T2-mapping MRI evaluation of patellofemoral cartilage in patients submitted to intra-articular platelet-rich plasma (PRP) injections

This study evaluated the ability of T2 mapping magnetic resonance imaging at 3 T, in addition to morphological sequences, to assess efficacy of platelet-rich plasma (PRP) injections, characterizing qualitatively and quantitatively the grade of knee cartilage repair in patients with patellofemoral chondropathy. We retrospectively studied 34 patients (22 men, 12 women, mean age 41.8 years, including 22 men) with patellofemoral knee chondropathy, who underwent intra-articular PRP injections and completed a clinical and instrumental follow-up. As control group, we evaluated 34 patients who underwent non-operative therapy. All patients were submitted to clinical (using VAS and WOMAC index) and imaging studies with 3 T magnetic resonance with cartilage analysis with T2 mapping sequences for cartilage analysis before and after treatment. In the study group, mean pre-treatment T2 relaxation time values were 44.2 ± 2.5 ms, considering all articular cartilage compartments, with significant reduction at the follow-up (p < 0.001). At the index compartment, mean pre-treatment T2 relaxation times values were 47.8 ± 3.6 ms, with statistically significant reduction at the follow-up (p < 0.001). Evaluation of focal cartilage lesions reported pre-treatment mean T2 value of 70.1 ± 13.0 ms and post-treatment mean value of 59.9 ± 4.6 ms (p < 0.001). From a clinical point of view, the pre-treatment WOMAC and VAS scores were 18.3 ± 4.5 and 7 (IQR:6-7.2), respectively; the post-treatment values were 7.3 ± 3.2 and 2 (IQR: 1.7-3.0), respectively (p < 0.001). In the control group, despite clinical improvement, we didn't find significant T2 values change during the follow-up period. In conclusion, T2 mapping is a valuable indicator for chondropathy and treatment-related changes over time.

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.

Chinese primary knee osteoarthritis progression cohort (CPKOPC) to evaluate the progression of knee osteoarthritis in the Beijing population: a prospective cohort study protocol

INTRODUCTION

Millions of patients are currently suffering from pain and dysfunction caused by osteoarthritis (OA), and billions of dollars have been invested into treatment. Because there is no effective treatment that can reverse the progression of knee OA, it is important to determine the risk factors that may influence the progression. However, although there are many studies that examine risk factors for progression, there are only a few that specifically focus on the impact of each risk factor for predicting progression of knee OA. This study aimed to develop a cohort of patients with primary knee OA in the Beijing area to establish models that identify the influence of each risk factor on the prediction of knee OA progression.

METHODS AND ANALYSIS

This is a prospective, multicentre, hospital-based cohort study. The study population comprises 2000 patients with primary knee OA from the Beijing area. The recruitment and baseline visits started in December 2017 and will finish in November 2018. After baseline visits, the patients will be followed for 3 years or until the occurrence of primary outcomes. Demographic variables will be collected during the baseline visit. Influencing factors including occupational exposures, family history and treatment will be collected at baseline and each follow-up visit. The primary outcome measure is a comprehensive index which will be combined with clinical WOMAC score, imaging K-L grade and clinical outcomes. These data will also be collected at baseline and each follow-up visit.

ETHICS AND DISSEMINATION

This study protocol has been approved by Peking University Third Hospital Medical Science Research Ethics Committee. All the eligible participants will give written informed consent. The findings will be published in peer-reviewed journals and presented at national or international conferences. Besides, the results will be disseminated to all participants via the social software 'WeChat'.

TRIAL REGISTRATION NUMBER

ChiCTR-ROC-17013790; preresults.

Quantitative MRI Musculoskeletal Techniques: An Update

OBJECTIVE. For many years, MRI of the musculoskeletal system has relied mostly on conventional sequences with qualitative analysis. More recently, using quantitative MRI applications to complement qualitative imaging has gained increasing interest in the MRI community, providing more detailed physiologic or anatomic information. CONCLUSION. In this article, we review the current state of quantitative MRI, technical and software advances, and the most relevant clinical and research musculoskeletal applications of quantitative MRI.

Imaging of osteoarthritis-recent research developments and future perspective

In osteoarthritis research, imaging plays an important role in clinical trials and epidemiological observational studies. In this narrative review article, we will describe recent developments in imaging of osteoarthritis in the research arena, mainly focusing on literature evidence published within the past 3 years (2014-2017). We will primarily focus on MRI including advanced imaging techniques that are not currently commonly used in routine clinical practice, although radiography, ultrasound and nuclear medicine (radiotracer) imaging will also be discussed. Research efforts to uncover the disease process of OA as well as to discover a disease modifying OA drug continue. MRI continues to play a large role in these endeavors, while compositional MRI techniques will increasingly become important due to their ability to assess "premorphologic" biochemical changes of articular cartilage and other tissues in and around joints. Radiography remain the primary imaging modality for defining inclusion/exclusion criteria as well as an outcome measure in OA clinical trials, despite known limitations for visualization of OA features. Compositional MRI techniques show promise for predicting structural and clinical outcomes in OA research. Ultrasound can be a useful adjunct to radiography and MRI particularly for evaluation of hand OA. Newer imaging techniques such as hybrid PET/MRI may have a potential but require further research and validation.

Subregional laminar cartilage MR spin-spin relaxation times (T2) in osteoarthritic knees with and without medial femorotibial cartilage loss - data from the Osteoarthritis Initiative (OAI)

OBJECTIVE

To explore whether subregional laminar femorotibial cartilage spin-spin relaxation time (T2) is associated with subsequent radiographic progression and cartilage loss and/or whether one-year change in subregional laminar femorotibial cartilage T2 is associated with concurrent progression in knees with established radiographic OA (ROA).

METHODS

In this case-control study, Osteoarthritis Initiative (OAI) knees with medial femorotibial progression were selected based on one-year loss in both quantitative cartilage thickness Magnetic resonance imaging (MRI) and radiographic joint space width (JSW). Non-progressor knees were matched by sex, Body mass index (BMI), baseline Kellgren-Lawrence-grade (2/3), and pain. Baseline and one-year follow-up superficial and deep cartilage T2 was analyzed in 16 femorotibial subregions using multi-echo spin-echo MRI.

RESULTS

37 knees showed medial femorotibial progression whereas 37 matched controls had no medial or lateral compartment progression. No statistically significant baseline differences between progressor and non-progressor knees in medial femorotibial cartilage T2 were observed in the superficial (48.9 ± 3.0 ms; 95% CI: [47.9, 49.9] vs 47.8 ± 3.6 ms; 95% CI: [46.6, 49.0], P = 0.07) or deep cartilage layer (40.8 ± 3.6 ms; 95% CI: [39.5, 42.0] vs 40.1 ± 4.7 ms; 95% CI: [38.5, 41.6], P = 0.29). Concurrent T2 change was more pronounced in the deep than the superficial cartilage layer. In the medial femorotibial compartment (MFTC), longitudinal change was greater in the deep layer of progressor than non-progressor knees (1.8 ± 4.5 ms; 95% CI: [0.3, 3.3] vs -0.2 ± 1.9 ms; 95% CI: [-0.8, 0.5], P = 0.02), whereas no difference was observed in the superficial layer.

CONCLUSION

Medial compartment cartilage T2 did not appear to be a strong prognostic factor for subsequent structural progression in the same compartment of knees with established ROA, when appropriately controlling for covariates. Yet, deep layer T2 change in the medial compartment occurred concurrent with medial femorotibial progression.

Skeletal Radiology: the year in review 2016

A look back at Skeletal Radiology in 2016 reveals a sizable number of publications that significantly advanced the state of knowledge about diseases of the musculoskeletal system. This review summarizes the content of some of the most intriguing papers of the year.

Advanced Imaging in Osteoarthritis

Context:

Radiography is widely accepted as the gold standard for diagnosing osteoarthritis (OA), but it has limitations when assessing early stage OA and monitoring progression. While there are improvements in the treatment of OA, the challenge is early recognition.

Evidence Acquisition:

MEDLINE and PubMed as well as professional orthopaedic and imaging websites were reviewed from 2006 to 2016.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

Magnetic resonance imaging (MRI) can provide the most comprehensive assessment of joint injury and OA with the advantages of being noninvasive and multiplanar with excellent soft tissue contrast. However, MRI is expensive, time consuming, and not widely used for monitoring OA clinically. Computed tomography (CT) and CT arthrography (CTA) can also be used to evaluate OA, but these are also invasive and require radiation exposure. Ultrasound is particularly useful for evaluation of synovitis but not for progression of OA.

Conclusion:

MRI, CT, and CTA are available for the diagnosis and monitoring of OA. Improvement in techniques and decrease in cost can allow some of these modalities to be effective methods of detecting early OA.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration

Context:

Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.

Evidence Acquisition:

A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.

Conclusion:

Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.