Magnetic resonance imaging-based biomarkers for knee osteoarthritis outcomes: A narrative review of prediction but not association studies.
Biomarker
Incidence
Knee osteoarthritis
MRI
Prediction
Progression
Total knee replacement
Journal
European journal of radiology
ISSN: 1872-7727
Titre abrégé: Eur J Radiol
Pays: Ireland
ID NLM: 8106411
Informations de publication
Date de publication:
10 Sep 2024
10 Sep 2024
Historique:
received:
23
05
2024
revised:
13
08
2024
accepted:
05
09
2024
medline:
15
9
2024
pubmed:
15
9
2024
entrez:
14
9
2024
Statut:
aheadofprint
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 39276401
pii: S0720-048X(24)00447-9
doi: 10.1016/j.ejrad.2024.111731
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
111731Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
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.