Thesis Format

Integrated Article

Quantitative Sonographic Assessment of Synovitis and its Relationship to Joint Damage and Pain in Knee Osteoarthritis

Author

Robert S. Dima, Western UniversityFollow

Degree

Doctor of Philosophy

Program

Health and Rehabilitation Sciences

Collaborative Specialization

Musculoskeletal Health Research

Supervisor

Appleton, Christopher T.

2nd Supervisor

Birmingham, Trevor

Co-Supervisor

Abstract

Osteoarthritis (OA) is a chronic condition affecting synovial joints, manifesting as pain, stiffness, and disability. Substantial evidence links inflammation of the synovial membrane (synovitis) to joint damage and knee pain, motivating the need for non-invasive and reproducible methods of synovitis assessment. The purpose of this thesis is to develop and test a new method of analyzing sonographic images of knee synovitis and, by integrating machine learning approaches, investigate the relationships between synovitis, joint damage, and OA-related knee pain.

Chapter two reviews imaging-based methods of synovitis assessment in knee OA, highlighting magnetic resonance and ultrasound as the most commonly used modalities. Various semi-quantitative and quantitative methods exist but lack consensus on a gold standard. Inconsistent correlations with knee pain and microscopic synovitis reinforce that synovitis represents both active inflammation and synovial remodeling.

Chapter three explores the measurement properties of a novel image analysis methodology for sonographic images of knee synovitis. This method separately quantifies effusion and synovitis without intravenous contrast, correlates strongly with existing measures, and is amenable to automation/pattern analysis using machine-learning (ML) methods.

Chapter four investigates the relationships between quantitative measures of synovitis, joint damage, and pain among patients with active early and late-stage knee OA. Synovial hyperplasia is more strongly associated with knee pain than effusion in early and late-stage disease. Inflammation in knee OA increases with joint damage in early-stage, but not in late-stage disease, which supports a theory of joint failure secondary to maladaptive synovial remodeling. These results have significant implications on our understanding of synovitis as an outcome measure and marker of joint homeostasis.

Chapter five employs ML to analyze the clustering behaviors of features representing radiographic OA severity, knee-specific pain, and synovitis. Three clinical phenotypes of knee OA are described- “early disease”, “active disease”, and “total joint failure”. Patients in the total joint failure group exhibit low synovitis alongside high pain and joint damage, with increased rates of systemic metabolic dysregulation.

Overall, these findings provide new insights into the complex relationships between inflammation, joint damage, and pain in knee OA, and open new avenues for ML in the processing and analysis of ultrasound-synovitis images.

Summary for Lay Audience

Osteoarthritis (OA) is a chronic disease causing pain, stiffness, and reduced mobility, with the knee being the most commonly affected joint. Recent data links inflammation (synovitis) to joint destruction and knee pain. Consequently, we need better ways to measure synovitis in the arthritic knee. This thesis aims to add to our knowledge about knee inflammation by i) summarizing current methods ii) developing and testing new methods and iii) exploring how inflammation relates to joint damage and knee pain.

Chapter two is a review of the literature, summarizing the current imaging-based methods of synovitis assessment in knee OA. Magnetic resonance imaging and ultrasound are the most commonly used methods, though there is no consensus on the gold standard. Conflicting results between otherwise similar studies lead us to believe that not all inflammation in knee OA is the same.

Chapter three presents a validation study for a new ultrasound analysis method tailored to knee synovitis. This method allows separate imaging of synovitis and joint swelling, can be applied to existing images, and correlates well with established research measures. Moreover, it's adaptable for enhancement through machine learning (ML).

Chapter four investigates inflammation levels across categories of joint damage and knee pain. Results indicate that thickening of the synovial membrane is more strongly related to pain than swelling, and that synovitis increases with joint damage in early OA but not in later stages. This suggests that a subset of individuals with knee OA have joint failure due to severe damage to the synovial membrane.

Chapter five employs ML to categorize knee OA patients based on joint damage, pain, and inflammation levels, identifying three groups: "early OA," "active OA," and "total joint failure." The latter, potentially representing patients with severe synovial damage, also exhibits increased cardiovascular disease risk factors.

Overall, these findings highlight the potential of ML in synovitis analysis and deepen our understanding of the interplay between inflammation, joint damage, and pain in knee OA.

Recommended Citation

Dima, Robert S., "Quantitative Sonographic Assessment of Synovitis and its Relationship to Joint Damage and Pain in Knee Osteoarthritis" (2024). Electronic Thesis and Dissertation Repository. 10089.
https://ir.lib.uwo.ca/etd/10089

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