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  • Title: Identifying MRI-detected inflammatory features specific for rheumatoid arthritis: two-fold feature reduction maintains predictive accuracy in clinically suspect arthralgia patients.
    Author: Aizenberg E, Ten Brinck RM, Reijnierse M, van der Helm-van Mil AHM, Stoel BC.
    Journal: Semin Arthritis Rheum; 2019 Feb; 48(4):579-586. PubMed ID: 29853189.
    Abstract:
    OBJECTIVE: MRI-detected inflammation is considered of diagnostic value for rheumatoid arthritis (RA), but its evaluation involves a time-consuming scoring of 61 joint-level features. It is not clear, however, which of these features are specific for RA and whether evaluating a subset of specific features is sufficient to differentiate RA patients. This study aimed to identify a subset of RA-specific features in a case-control setting and validate them in a longitudinal cohort of arthralgia patients. METHODS: The difference in frequency of MRI-detected inflammation (bone marrow edema, synovitis, and tenosynovitis) between 199 RA patients and 193 controls was studied in 61 features across the wrist, metacarpophalangeal, and metatarsophalangeal joints. A subset of RA-specific features was obtained by applying a cutoff on the frequency difference while maximizing discriminative performance. For validation, this subset was used to predict arthritis development in 225 clinically suspect arthralgia (CSA) patients. Diagnostic performance was compared to a reference method that uses the complete set of 61 features normalized for inflammation levels in age-matched controls. RESULTS: Subset of 30 features, mainly (teno)synovitis, was obtained from the case-control setting. Validation in CSA patients yielded an area of 0.69 (95% CI: 0.59-0.78) under the ROC curve and a positive predictive value (PPV) of 31%, compared to 0.68 (95% CI: 0.60-0.77) and 29% PPV of the reference method with 61 features. CONCLUSION: Subset of 30 MRI-detected inflammatory features, dominated by (teno)synovitis, offers a considerable reduction of scoring efforts without compromising accuracy for prediction of arthritis development in CSA patients.
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