MRI-defined tissue damage as predictors of the incidence and progression of knee osteoarthritis: A systematic review
Mohammadi S, Harandi H, Alikarami S, fattahniya S, Samiee R, Ghavam M, Karimi E, Jahanshahi A, Roemer FW, Hunter DJ, Guermazi A (2026)
Publication Type: Journal article, Review article
Publication year: 2026
Journal
Book Volume: 79
Article Number: 152999
DOI: 10.1016/j.semarthrit.2026.152999
Abstract
Background: MRI is increasingly recognized not only for visualization of knee joint structures in knee osteoarthritis (KOA), but also for its potential to predict KOA incidence and progression. Objective: We aim to provide a comprehensive overview of how different types of MRI-detected joint tissue pathology perform in predicting radiographic progression and longitudinal evolution of clinical outcomes and functional decline. Methods: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD420251132451). A systematic literature search was performed in PubMed, Scopus, and Web of Science. After removal of duplicates, 4810 studies underwent a multi-step screening process, of which 99 were included in the qualitative synthesis. The quality of included studies was evaluated using the Newcastle-Ottawa Scale and the Downs and Black checklist. Results: Most of the included studies were of good quality. Strong predictors of KOA incidence included baseline bone marrow lesions (BMLs), specific bone shape patterns (ORs up to 12.5 (95% CI, 4.0–39.3)), meniscal tears, and synovitis. Predictors of KOA progression, characterized by increasing cartilage damage, were meniscal extrusion, synovitis, and BMLs. Notably, baseline cartilage T2 signal abnormalities were a powerful predictor of the future development of new structural cartilage defects (OR = 21.3 (95% CI, 11.1, 40.6)), highlighting a pathway from compositional to structural deterioration in knees with and without pre-existing disease. Conclusion: Several MRI-detected joint tissue pathologies longitudinally associated with structural progression and clinically relevant outcomes, such as total knee arthroplasty, allowing patient stratification for disease-modifying osteoarthritis drug (DMOAD) trials. These associations may be further strengthened using compositional and multi-featured MRI models as well as AI-based feature extraction.
Involved external institutions
Washington University in St. Louis
United States (USA) (US)
Tehran University of Medical Sciences (TUMS) / دانشگاه علوم پزشکی تهران
Iran, Islamic Republic of (IR)
Royal North Shore Hospital (RNSH)
Australia (AU)
Boston University
United States (USA) (US)
Iran University of Medical Sciences (IUMS) / دانشگاه علوم پزشکی ایران
Iran, Islamic Republic of (IR)
United States (USA) (US)
Tehran University of Medical Sciences (TUMS) / دانشگاه علوم پزشکی تهران
Iran, Islamic Republic of (IR)
Royal North Shore Hospital (RNSH)
Australia (AU)
Boston University
United States (USA) (US)
Iran University of Medical Sciences (IUMS) / دانشگاه علوم پزشکی ایران
Iran, Islamic Republic of (IR)
How to cite
APA:
Mohammadi, S., Harandi, H., Alikarami, S., fattahniya, S., Samiee, R., Ghavam, M.,... Guermazi, A. (2026). MRI-defined tissue damage as predictors of the incidence and progression of knee osteoarthritis: A systematic review. Seminars in Arthritis and Rheumatism, 79. https://doi.org/10.1016/j.semarthrit.2026.152999
MLA:
Mohammadi, Soheil, et al. "MRI-defined tissue damage as predictors of the incidence and progression of knee osteoarthritis: A systematic review." Seminars in Arthritis and Rheumatism 79 (2026).
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