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Article Abstract

Rationale And Objectives: To assess the diagnostic power of MR imaging features for post-traumatic osteomyelitis (PTOM) of the extremities with metal implants using a 3.0-T machine with conventional metal-artifact reducing (MAR) protocols.

Materials And Methods: Between December 2012 and September 2016, 261 consecutive patients underwent MRI for clinical suspicion of PTOM in the appendicular skeleton at our hospital. A total of 72 enrolled patients with metal implants were divided into two groups, one with surgical, histologic, or microbiologic proven to be PTOM, and the other proven not to be PTOM. Their MR images were reviewed by two musculoskeletal radiologists who qualitatively analyzed various findings regarding PTOM, and concluded diagnosis of PTOM or without PTOM for each patient. All MR images were obtained using conventional MAR protocols.

Results: The sensitivity, specificity, positive and negative predictive values, and accuracy of MR diagnosis by two observers were 55%/38%, 81%/93%, 67%/40%, 73%/78%, and 71%/71%, respectively. Among the findings, T1 low signal intensity, medullary location, confluent pattern, typical features (concurrence of T1 low signal intensity, medullary, and confluent patterns), heterogeneous or no enhancement, and cortical destruction were statistically significant by univariate analysis (p<0.05). Among these features, only medullary involvement was significant by multivariate analysis (p = 0.007). Medullary involvement and no enhancement pattern were significant by step-wise discrimination analysis. Interobserver correlation was moderate with a weighted kappa value of 0.512 (confidence interval: 0.3-0.723).

Conclusion: The overall accuracy for diagnosis of PTOM of the extremities using 3.0-T MRI with conventional metal-artifact reduction parameters was 71%. The strongest diagnostic MR imaging feature was medullary involvement of T1 low signal intensity. PTOM of the extremities can be accurately diagnosed with a 3.0-T MR machine using conventional MAR protocols.

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http://dx.doi.org/10.1016/j.acra.2019.01.001DOI Listing

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