Defining and predicting radiographic knee osteoarthritis progression: a systematic review of findings from the osteoarthritis initiative

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

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

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.

Expanding from unilateral to bilateral: A robust deep learning-based approach for predicting radiographic osteoarthritis progression

OBJECTIVE

To develop and validate a deep learning (DL) model for predicting osteoarthritis (OA) progression based on bilateral knee joint views.

METHODS

In this retrospective study, knee joints from bilateral posteroanterior knee radiographs of participants in the Osteoarthritis Initiative were analyzed. At baseline, participants were divided into testing set 1 and development set according to the different enrolled sites. The development set was further divided into a training set and a validation set in an 8:2 ratio for model development. At 48-month follow-up, eligible patients were formed testing set 2. The Bilateral Knee Neural Network (BikNet) was developed using bilateral views, with the knee to be predicted as the main view and the contralateral knee as the auxiliary view. DenseNet and ResNext were also trained and compared as the unilateral model. Two reader tests were conducted to evaluate the model's value in predicting incident OA.

RESULTS

Totally 3583 participants were evaluated. The BikNet we proposed outperformed ResNext and DenseNet (all area under the curve [AUC] < 0.71, P < 0.001) with AUC values of 0.761 and 0.745 in testing sets 1 and 2, respectively. With assistance of the BikNet increased clinicians' sensitivity (from 28.1-63.2% to 42.1-68.4%) and specificity (from 57.4-83.4% to 64.1-87.5%) of incident OA prediction and improved inter-observer reliability.

CONCLUSION

The DL model, constructed based on bilateral knee views, holds promise for enhancing the assessment of OA and demonstrates greater robustness during subsequent follow-up evaluations as compared with unilateral models. BikNet represents a potential tool or imaging biomarker for predicting OA progression.

A clinical model to predict the progression of knee osteoarthritis: data from Dryad

BACKGROUND

Knee osteoarthritis (KOA) is a multifactorial, slow-progressing, non-inflammatory degenerative disease primarily affecting synovial joints. It is usually induced by advanced age and/or trauma and eventually leads to irreversible destruction of articular cartilage and other tissues of the joint. Current research on KOA progression has limited clinical application significance. In this study, we constructed a prediction model for KOA progression based on multiple clinically relevant factors to provide clinicians with an effective tool to intervene in KOA progression.

METHOD

This study utilized the data set from the Dryad database which included patients with Kellgren-Lawrence (KL) grades 2 and 3. The KL grades was determined as the dependent variable, while 15 potential predictors were identified as independent variables. Patients were randomized into training set and validation set. The training set underwent LASSO analysis, model creation, visualization, decision curve analysis and internal validation using R language. The validation set is externally validated and F1-score, precision, and recall are computed.

RESULT

A total of 101 patients with KL2 and 94 patients with KL3 were selected. We randomly split the data set into a training set and a validation set by 8:2. We filtered "BMI", "TC", "Hypertension treatment", and "JBS3 (%)" to build the prediction model for progression of KOA. Nomogram used to visualize the model in R language. Area under ROC curve was 0.896 (95% CI 0.847-0.945), indicating high discrimination. Mean absolute error (MAE) of calibration curve = 0.041, showing high calibration. MAE of internal validation error was 0.043, indicating high model calibration. Decision curve analysis showed high net benefit. External validation of the metabolic syndrome column-line graph prediction model was performed by the validation set. The area under the ROC curve was 0.876 (95% CI 0.767-0.984), indicating that the model had a high degree of discrimination. Meanwhile, the calibration curve Mean absolute error was 0.113, indicating that the model had a high degree of calibration. The F1 score is 0.690, the precision is 0.667, and the recall is 0.714. The above metrics represent a good performance of the model.

CONCLUSION

We found that KOA progression was associated with four variable predictors and constructed a predictive model for KOA progression based on the predictors. The clinician can intervene based on the nomogram of our prediction model.

KEY INFORMATION

This study is a clinical predictive model of KOA progression. KOA progression prediction model has good credibility and clinical value in the prevention of KOA progression.

Externally validated models for first diagnosis and risk of progression of knee osteoarthritis

OBJECTIVE

We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST).

MATERIALS AND METHODS

The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively.

RESULTS

The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721-0.774) and 0.670 (0.631-0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325-0.7439) and in external validation 0.72 (0.7190-0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data.

DISCUSSION

The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years.

CONCLUSION

Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.