MRI-defined Osteophyte Presence and Concomitant Cartilage Damage in Knees with Incident Tibiofemoral Osteoarthritis: Data From The Pivotal Osteoarthritis Initiative Magnetic Resonance Imaging Analyses (POMA) Study

Knee cartilage change on magnetic resonance imaging: Should we lump or split topographical regions? A 2-year study of data from the osteoarthritis initiative

OBJECTIVE

We challenge the paradigm that a simplistic approach evaluating anatomic regions (e.g., medial femur or tibia) is ideal for assessing articular cartilage loss on magnetic resonance (MR) imaging. We used a data-driven approach to explore whether specific topographical locations of knee cartilage loss may identify novel patterns of cartilage loss over time that current assessment strategies miss.

DESIGN

We assessed 60 location-specific measures of articular cartilage on a sample of 99 knees with baseline and 24-month MR images from the Osteoarthritis Initiative, selected as a group with a high likelihood to change. We performed factor analyses of the change in these measures in two ways: (1) summing the measures to create one measure for each of the six anatomically regional-based summary (anatomic regions; e.g., medial tibia) and (2) treating each location separately for a total of 60 measures (location-specific measures).

RESULTS

The first analysis produced three factors accounting for 66% of the variation in the articular cartilage changes that occur over 24 months of follow-up: (1) medial tibiofemoral, (2) medial and lateral patellar, and (3) lateral tibiofemoral. The second produced 20 factors accounting for 75% of the variance in cartilage changes. Twelve factors only involved one anatomic region. Five factors included locations from adjoining regions (defined by the first analysis; e.g., medial tibiofemoral). Three factors included articular cartilage loss from disparate locations.

CONCLUSIONS

Novel patterns of cartilage loss occur within each anatomic region and across these regions, including in disparate regions. The traditional anatomic regional approach is simpler to implement and interpret but may obscure meaningful patterns of change.

Osteoarthritis year in review 2023: Imaging

PURPOSE

This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023.

METHODS

A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review.

RESULTS

Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest.

CONCLUSIONS

Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.

Metabolic and inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion

OBJECTIVES

Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high heterogeneity. In this study, we aimed to identify different phenotypes in knee osteoarthritis (KOA) patients with joint effusion based on their metabolic and inflammatory profiles.

METHODS

A non-supervised strategy based on statistical and machine learning methods was applied to 45 parameters measured on 168 female KOA patients with persistent joint effusion, consecutively recruited at our hospital after a monographic OA outpatient visit. Data comprised anthropometric and metabolic factors and a panel of systemic and local inflammatory markers. The resulting clusters were compared regarding their clinical, radiographic and ultrasound severity at baseline and their radiographic progression at two years.

RESULTS

Our analyses identified four KOA inflammatory phenotypes (KOIP): a group characterized by metabolic syndrome, probably driven by body fat and obesity, and by high local and systemic inflammation (KOIP-1); a metabolically healthy phenotype with mild overall inflammation (KOIP-2); a non-metabolic phenotype with high inflammation levels (KOIP-3); and a metabolic phenotype with low inflammation and cardiovascular risk factors not associated with obesity (KOIP-4). Of interest, these groups exhibited differences regarding pain, functional disability and radiographic progression, pointing to a clinical relevance of the uncovered phenotypes.

CONCLUSION

Our results support the existence of different KOA phenotypes with clinical relevance and differing pathways regarding their pathophysiology and disease evolution, which entails implications in patients' stratification, treatment tailoring and the search of novel and personalized therapies.