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Rationale: Although several functional studies have demonstrated that positron emission tomography/computed tomography with F-fluorodeoxyglucose (F-FDG PET/CT) appears to be efficient to identify a cerebral substrate in patients with known macrophagic myofasciitis (MMF), the predictive value of this imaging technique for MMF remains unclear., Patient Concerns: We presented data and images of a 46-year-old woman., Diagnoses: The patient was referred to our center for suspected MMF due to diffuse arthromyalgias and cognitive disorder (involving an impairment of visual selective attention and a weakness in executive functions revealed by neuropsychological assessment) which occurred few years after last vaccine injections., Interventions: After a first negative deltoid muscle biopsy, a brain F-FDG PET/CT was performed and revealed the known spatial pattern of a cerebral glucose hypometabolism involving occipital cortex, medial temporal areas, and cerebellum., Outcomes: Given the clinical suspicion of MMF and brain F-FDG PET/CT findings, a 2nd deltoid muscle biopsy was performed and confirmed the diagnosis of MMF with typical histopathological features., Lessons: This case highlights the predictive value of brain F-FDG PET/CT as a noninvasive imaging tool for MMF diagnosis, even when muscle biopsy result comes back negative.

Introduction: Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles., Methods: 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated., Results: The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%., Conclusion: We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.