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

Autor: Herrera D; Translational Medicine Research Platform, PRIMMO, University Hospital Centre of Orleans, 45100 Orleans, France., Almhdie-Imjabbar A; Translational Medicine Research Platform, PRIMMO, University Hospital Centre of Orleans, 45100 Orleans, France., Toumi H; Translational Medicine Research Platform, PRIMMO, University Hospital Centre of Orleans, 45100 Orleans, France; Department of Rheumatology, University Hospital Centre of Orleans, 45100 Orleans, France., Lespessailles E; Translational Medicine Research Platform, PRIMMO, University Hospital Centre of Orleans, 45100 Orleans, France; Department of Rheumatology, University Hospital Centre of Orleans, 45100 Orleans, France. Electronic address: eric.lespessailles@chr-orleans.fr.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2024 Sep 10; Vol. 181, pp. 111731. Date of Electronic Publication: 2024 Sep 10.
DOI: 10.1016/j.ejrad.2024.111731
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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