Osteoarthritis year in review 2022: imaging

Magnetic resonance imaging-based biomarkers for knee osteoarthritis outcomes: A narrative review of prediction but not association studies

BACKGROUND

Magnetic Resonance Imaging (MRI) is frequently used in recent studies on knee osteoarthritis (KOA), focusing on developing innovative MRI-based biomarkers to predict KOA outcomes. The growing volume of publications devoted to this subject highlights the need for an up-to-date review.

METHODS

In this narrative review, we utilized the PubMed database to identify studies examining MRI-based biomarkers for the prediction of knee osteoarthritis (KOA), focusing on those reporting relevant prediction, not association, metrics. The identified articles were subsequently categorized into three distinct outcomes: Prediction of KOA incidence (KOAi), KOA progression (KOAp) and total knee arthroplasty risk (TKAr). Within each category, results were organized by the nature of biomarker(s) used, as either quantitative, semi-quantitative or compound.

RESULTS

Due to the lack of predictive metrics such as the area under the ROC curve (AUC) scores, sensitivity or specificity, 27 studies were excluded. A final set of 23 studies were deemed eligible for our analysis. The mean AUC scores reported ranged from 0.67 to 0.83 for predicting KOAi, 0.54 to 0.84 for KOAp and 0.55 to 0.94 for TKAr. Excellent predictive performance (AUC>0.8) was observed for the prediction of radiographic KOAi, KOAp and TKAr when using cartilage and meniscal-based measures, osteophyte scores and infrapatellar fat pad texture, and bone marrow lesions, respectively.

CONCLUSION

The results showed that numerous studies highlighted the importance of MRI-based biomarkers as promising predictors of the three key outcomes. In addition, this narrative review also emphasized the necessity for KOA prediction studies to include adequate reporting of predictive metrics.

Knowledge mapping and bibliometric analysis of medical knee magnetic resonance imaging for knee osteoarthritis (2004-2023)

Objectives

Magnetic resonance imaging (MRI) is increasingly used to detect knee osteoarthritis (KOA). In this study, we aimed to systematically examine the global research status on the application of medical knee MRI in the treatment of KOA, analyze research hotspots, explore future trends, and present results in the form of a knowledge graph.

Methods

The Web of Science core database was searched for studies on medical knee MRI scans in patients with KOA between 2004 and 2023. CiteSpace, SCImago Graphica, and VOSviewer were used for the country, institution, journal, author, reference, and keyword analyses.

Results

A total of 2,904 articles were included. The United States and Europe are leading countries. Boston University is the main institution. Osteoarthritis and cartilage is the main magazine. The most frequently cocited article was "Radiological assessment of osteoarthrosis". Guermazi A was the author with the highest number of publications and total references. The keywords most closely linked to MRI and KOA were "cartilage", "pain", and "injury".

Conclusions

The application of medical knee MRI in KOA can be divided into the following parts: (1). MRI was used to assess the relationship between the characteristics of local tissue damage and pathological changes and clinical symptoms. (2).The risk factors of KOA were analyzed by MRI to determine the early diagnosis of KOA. (3). MRI was used to evaluate the efficacy of multiple interventions for KOA tissue damage (e.g., cartilage defects, bone marrow edema, bone marrow microfracture, and subchondral bone remodeling). Artificial intelligence, particularly deep learning, has become the focus of research on MRI applications for KOA.

What is New in Osteoarthritis Imaging?

Osteoarthritis (OA) is the leading joint disorder globally, affecting a significant proportion of the population. Recent studies have changed our understanding of OA, viewing it as a complex pathology of the whole joint with a multifaceted etiology, encompassing genetic, biological, and biomechanical elements. This review highlights the role of imaging in diagnosing and monitoring OA. Today's role of radiography is discussed, while also elaborating on the advances in computed tomography and magnetic resonance imaging, discussing semiquantitative methods, quantitative morphologic and compositional techniques, and giving an outlook on the potential role of artificial intelligence in OA research.

Radiomics analysis using magnetic resonance imaging of bone marrow edema for diagnosing knee osteoarthritis

This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis. Clinical and magnetic resonance imaging (MRI) data of 302 patients with and without osteoarthritis were retrospectively collected from April 2022 to October 2023 at Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine. The participants were randomly divided into two groups (a training group, n = 211 and a testing group, n = 91). We used logistic regression to analyze clinical characteristics and established a clinical model. Radiomics signatures were developed by extracting radiomic features from the bone marrow edema area using MRI. A nomogram was developed based on the rad-score and clinical characteristics. The diagnostic performance of the three models was compared using the receiver operating characteristic curve and Delong's test. The accuracy and clinical application value of the nomogram were evaluated using calibration curve and decision curve analysis. Clinical characteristics such as age, radiographic grading, Western Ontario and McMaster Universities Arthritis Index score, and radiological features were significantly correlated with the diagnosis of osteoarthritis. The Rad score was constructed from 11 radiological features. A clinical model was developed to diagnose osteoarthritis (training group: area under the curve [AUC], 0.819; testing group: AUC, 0.815). Radiomics models were used to effectively diagnose osteoarthritis (training group,: AUC, 0.901; testing group: AUC, 0.841). The nomogram model composed of Rad score and clinical characteristics had better diagnostic performance than a simple clinical model (training group: AUC, 0.906; testing group: AUC, 0.845; p < 0.01). Based on DCA, the nomogram model can provide better diagnostic performance in most cases. In conclusion, the MRI-bone marrow edema-based radiomics-clinical nomogram model showed good performance in diagnosing early osteoarthritis.

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.

Epidemiology of osteoarthritis: literature update 2022-2023

PURPOSE OF REVIEW

This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods.

RECENT FINDINGS

Contemporary research has improved our understanding of the burden of OA in typically understudied regions, including ethnic and racial minorities in high-income countries, the Middle East and North Africa (MENA) and Latin America. Efforts have also been made to explore the burden and risk factors in OA in previously understudied joints, such as the hand, foot, and ankle. Advancements in OA imaging techniques have occurred alongside the developments of AI methods aiming to predict disease phenotypes, progression, and outcomes.

SUMMARY

Continuing efforts to expand our knowledge around OA in understudied populations will allow for the creation of targeted and specific interventions and inform policy changes aimed at reducing disease burden in these groups. The burden and disability associated with OA is notable in understudied joints, warranting further research efforts that may lead to effective therapeutic options. AI methods show promising results of predicting OA phenotypes and progression, which also may encourage the creation of targeted disease modifying OA drugs (DMOADs).

Bio-responsive and multi-modality imaging nanomedicine for osteoarthritis theranostics

Osteoarthritis (OA) is one of the most common joint diseases currently, characterized by the gradual degradation of cartilage, remodeling of subchondral bone, development of synovitis, degenerative alterations in the menisci, and formation of osteophytes. Generally, loss of articular cartilage is the most common pathological manifestation of OA. However, owing to the lack of blood vessels and nerves, the damaged cartilage is unable to execute self-repair. Therefore, early detection and treatment of cartilage lesions are extremely vital. Given that precise diagnosis and therapeutic strategy are indispensable from the basic pathological features of OA, an ideal therapeutic strategy should cater to the specific features of the OA microenvironment to achieve disease-modifying therapy. To date, nanomedicine presents an opportunity to achieve the precisely targeted delivery of agents and stimuli-sensitive release at the optimum dose, which may be coupled with a controlled release profile and reduced side effects. This review mainly summarizes inherent and microenvironment traits of OA and outlines stimuli-responsive nanotherapies, including internal bio-responsive (e.g., reactive oxygen species, pH, and protease) and external (e.g., photo stimuli, temperature, ultrasound, and magnetic field) responsive nanotherapies. Furthermore, multi-targeted therapeutic strategies combined with multi-modality imaging are also discussed. In general, future exploration of more novel stimuli-responsive nanotherapies that can be used for early diagnosis and cartilage targeting may help ameliorate OA-related cartilage damage, decrease pain, and promote joint function.