FDG-PET/CT Brain Findings in a Patient With Macrophagic Myofasciitis

Myalgia and chronic fatigue syndrome following immunization: macrophagic myofasciitis and animal studies support linkage to aluminum adjuvant persistency and diffusion in the immune system

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multifactorial and poorly undersood disabling disease. We present epidemiological, clinical and experimental evidence that ME/CFS constitutes a major type of adverse effect of vaccines, especially those containing poorly degradable particulate aluminum adjuvants. Evidence has emerged very slowly due to the multiplicity, lack of specificity, delayed onset, and frequent medical underestimation of ME/CFS symptoms. It was supported by an epidemiological study comparing vaccinated vs unvaccinated militaries that remained undeployed during Gulf War II. Affected patients suffer from cognitive dysfunction affecting attention, memory and inter-hemispheric connexions, well correlated to brain perfusion defects and associated with a stereotyped and distinctive pattern of cerebral glucose hypometabolism. Deltoid muscle biopsy performed to investigate myalgia typically yields macrophagic myofasciitis (MMF), a histological biomarker assessing longstanding persistency of aluminum agglomerates within innate immune cells at site of previous immunization. MMF is seemingly linked to altered mineral particle detoxification by the xeno/autophagy machinery. Comparing toxicology of different forms of aluminum and different types of exposure is misleading and inadequate and small animal experiments have turned old dogma upside down. Instead of being rapidly solubilized in the extracellular space, injected aluminum particles are quickly captured by immune cells and transported to distant organs and the brain where they elicit an inflammatory response and exert selective low dose long-term neurotoxicity. Clinical observations and experiments in sheep, a large animal like humans, confirmed both systemic diffusion and neurotoxic effects of aluminum adjuvants. Post-immunization ME/CFS represents the core manifestation of "autoimmune/inflammatory syndrome induced by adjuvants" (ASIA).

Macrophagic myofasciitis: a challenging diagnosis

A 25-year-old man admitted for generalised muscle pain with an insidious onset 3 years ago. He had exercise intolerance and decrease in muscle strength, requiring gait support. He was previously healthy, with no chronic medication or recent history of drugs or toxics. National vaccination plan actualised with hepatitis B and tetanus vaccines administered 10 and 2 years, respectively, before symptom onset. No analytical, imaging or electromyography changes were found. Muscle biopsy revealed an inflammatory infiltrate predominantly macrophagic with aluminium deposits suggestive of macrophagic myofasciitis (MMF). It is probably associated with vaccines previously administered. MMF lesion can be regarded as pathological only if detected at least 18 months after last aluminic immunisation, as our case illustrates.

Predictive value of brain 18F-FDG PET/CT in macrophagic myofasciitis?: A case report

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.

Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach

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.