Are structural abnormalities on knee MRI associated with osteophyte development? Data from the Osteoarthritis Initiative

Predictive models of radiographic progression and pain progression in patients with knee osteoarthritis: data from the FNIH OA biomarkers consortium project

OBJECTIVES

The progression of knee osteoarthritis (OA) can be defined as either radiographic progression or pain progression. This study aimed to construct models to predict radiographic progression and pain progression in patients with knee OA.

METHODS

We retrieved data from the FNIH OA Biomarkers Consortium project, a nested case-control study. A total of 600 subjects with mild to moderate OA (Kellgren-Lawrence grade of 1, 2, or 3) in one target knee were enrolled. The patients were classified as radiographic progressors (n = 297), non-radiographic progressors (n = 303), pain progressors (n = 297), or non-pain progressors (n = 303) according to the change in the minimum joint space width of the medial compartment and the WOMAC pain score during the follow-up period of 24-48 months. Initially, 376 variables concerning demographics, clinical questionnaires, imaging measurements, and biochemical markers were included. We developed predictive models based on multivariate logistic regression analysis and visualized the models with nomograms. We also tested whether adding changes in predictors from baseline to 24 months would improve the predictive efficacy of the models.

RESULTS

The predictive models of radiographic progression and pain progression consisted of 8 and 10 variables, respectively, with area under curve (AUC) values of 0.77 and 0.76, respectively. Incorporating the change in the WOMAC pain score from baseline to 24 months into the pain progression predictive model significantly improved the predictive effectiveness (AUC = 0.86).

CONCLUSIONS

We identified risk factors for imaging progression and pain progression in patients with knee OA over a 2- to 4-year period, and provided effective predictive models, which could help identify patients at high risk of progression.

The mediating role of synovitis in meniscus pathology and knee osteoarthritis radiographic progression

Meniscus pathologies (damage, extrusion) and synovitis are associated with knee osteoarthritis (KOA); however, whether synovitis mediates the relationship between meniscus pathologies and KOA radiographic progression remains unclear. We conducted an observational study in the Osteoarthritis Initiative (OAI) cohort, with a 48-month follow-up. Meniscus pathology and synovitis were measured by MRI osteoarthritis knee score (MOAKS) at baseline and 24 months, and a comprehensive synovitis score was calculated using effusion and Hoffa synovitis scores. The knee osteoarthritis radiographic progression was considered that Kellgren-Lawrence (KL) grade and joint space narrowing (JSN) grade at 48 months were increased compared to those at baseline. This study included a total of 589 participants, with KL grades mainly being KL1 (26.5%), KL2 (34.1%), and KL3 (30.2%) at baseline, while JSN grades were mostly 0 at baseline. A logistic regression model was used to analyze the relationship between meniscus pathology, synovitis, and KOA progression. Mediation analysis was used to evaluate the mediation effect of synovitis. The average age of the participants was 61 years old, 62% of which were female. The medial meniscus extrusion was longitudinally correlated with the progression of KL (odds ratio [OR]: 2.271, 95% confidence interval [CI]: 1.412-3.694) and medial JSN (OR: 3.211, 95% CI: 2.040-5.054). Additionally, the longitudinal correlation between medial meniscus damage and progression of KOA (OR: 1.853, 95% CI: 1.177-2.941) and medial JSN (OR: 1.655, 95% CI: 1.053-2.602) was significant. Synovitis was found to mediate the relationship between medial meniscus extrusion and KL and medial JSN progression at baseline (β: 0.029, 95% CI: 0.010-0.053; β: 0.022, 95% CI: 0.005-0.046) and beyond 24 months (β: 0.039, 95% CI: 0.016-0.068; β: 0.047, 95% CI: 0.020-0.078). However, we did not find evidence of synovitis mediating the relationship between meniscal damage and KOA progression. Synovitis mediates the relationship between medial meniscus extrusion (rather than meniscus damage) and KOA progression.

Medial meniscus extrusion is invariably observed and consistent with tibial osteophyte width in elderly populations: The Bunkyo Health Study

We reported that the full-length width of medial tibial osteophytes comprising cartilage and bone parts correlates with medial meniscus extrusion (MME) in early-stage knee osteoarthritis (OA). However, no data exist on the prevalence of MME and its relationship with osteophytes in the elderly population. 1191 elderly individuals (females 57%; 72.9 years old on average) in the Bunkyo Health Study underwent standing plain radiograph and proton density-weighted MRI on knee joints. MRI-detected OA changes were evaluated according to the Whole-Organ Magnetic Resonance Imaging Score. A new method of assessing the cartilage and bone parts of osteophytes was developed using pseudo-coloring images of proton density-weighted fat-suppressed MRI. Most subjects showed Kellgren-Lawrence grade 1 or 2 radiographic medial knee OA (88.1%), MME (98.7%, 3.90 ± 2.01 mm), and medial tibial osteophytes (99.3%, 3.27 ± 1.50 mm). Regarding OA changes, MME was closely associated with the full-length width of medial tibial osteophytes (β = 1.114; 95% CI 1.069-1.159; p < 0.001) in line with osteophyte width (intraclass correlation coefficient, 0.804; 95% CI 0.783-0.823). Our data revealed that MME and medial tibial osteophytes are observed in the elderly and demonstrate that the degree of MME is consistent with the full-length width of medial tibial osteophytes, suggesting that osteophytes might be implicated in MME.

Anterior meniscus extrusion is associated with anterior tibial osteophyte width in knee osteoarthritis - The Bunkyo Health Study

Background

In knee osteoarthritis (OA), medial meniscus extrudes both medially and anteriorly. We reported that full-length width of medial tibial osteophyte, which comprises cartilage and bone parts, is directly associated with medial meniscus extrusion in early-stage knee OA and hypothesized that anterior tibial osteophyte (ATO) is also associated with anterior meniscus extrusion (AME). Thus, we aimed to examine their prevalence and relationship.

Methods

Elderly subjects (638 females and 507 males; average 72.9 years old) in the Bunkyo Health Study cohort were enrolled. MRI-detected OA changes were evaluated according to the Whole Organ Magnetic Resonance Imaging Score. ATO was evaluated using the method which can assess both cartilage and bone parts of osteophyte by pseudo-coloring images of proton density-weighted fat-suppressed MRI.

Results

Most subjects showed the Kellgren-Lawrence grade 1/2 of the medial knee OA (88.1%), AME (94.3%, 3.7 ​± ​2.2 ​mm), and ATO (99.6%, 4.2 ​± ​1.5 ​mm). Among the OA changes, AME was most closely associated with full-length width of ATO (multivariable β ​= ​0.877, p ​< ​0.001). The area under the receiver operating characteristic curve for determining the presence of AME as evaluated by ATO width was 0.75 (95% confidence interval 0.60-0.84, p ​< ​0.001). The odds ratio for the presence of AME as evaluated by ATO width at 2.9 ​mm was 7.16 (4.23-12.15, p ​< ​0.001, age, gender, BMI, and K-L adjusted).

Conclusions

AME and ATO were inevitably observed in the elderly subjects and AME was closely associated with full-length width of ATO. Our study provides the first evidence on the close relationship between AME and ATO in knee OA.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

A Multi-Task Deep Learning Method for Detection of Meniscal Tears in MRI Data from the Osteoarthritis Initiative Database

We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on complete 3D MRI scans. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data and how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences.