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

<div><p>Introduction</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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%.</p><p>Conclusion</p><p>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.</p></div

Comparison between ¹⁸F-FDG PET brain images of 100 MMF patients and 44 healthy subjects included in the training set.

<p>Brain areas with significant decreased uptake of ¹⁸F-FDG served as mask to train the support vector machine classifier. Results were collected at a P-value < 0.005 at the voxel level, for clusters k ≥ 200 voxels with adjustment for age.</p

Statistical parametric mapping (SPM) and support vector machine (SVM) procedures.

<p>MMF, Macrophagic myofasciitis; L, left; R, right.</p

Box plots of the dot multiplication in training and testing populations.

<p>Box plots of the dot multiplication in training and testing populations.</p

Population characteristics for the training and testing groups.

<p>Population characteristics for the training and testing groups.</p

Confusion matrix of the result of the support vector machine classifier for the diagnosis.

<p>Confusion matrix of the result of the support vector machine classifier for the diagnosis.</p

Neuropsychological Correlates of Brain Perfusion SPECT in Patients with Macrophagic Myofasciitis

<div><p>Background</p><p>Patients with aluminum hydroxide adjuvant-induced macrophagic myofasciitis (MMF) complain of arthromyalgias, chronic fatigue and cognitive deficits. This study aimed to characterize brain perfusion in these patients.</p><p>Methods</p><p>Brain perfusion SPECT was performed in 76 consecutive patients (aged 49±10 y) followed in the Garches-Necker-Mondor-Hendaye reference center for rare neuromuscular diseases. Images were acquired 30 min after intravenous injection of 925 MBq ^99mTc-ethylcysteinate dimer (ECD) at rest. All patients also underwent a comprehensive battery of neuropsychological tests, within 1.3±5.5 mo from SPECT. Statistical parametric maps (SPM12) were obtained for each test using linear regressions between each performance score and brain perfusion, with adjustment for age, sex, socio-cultural level and time delay between brain SPECT and neuropsychological testing.</p><p>Results</p><p>SPM analysis revealed positive correlation between neuropsychological scores (mostly exploring executive functions) and brain perfusion in the posterior associative cortex, including cuneus/precuneus/occipital lingual areas, the periventricular white matter/corpus callosum, and the cerebellum, while negative correlation was found with amygdalo-hippocampal/entorhinal complexes. A positive correlation was also observed between brain perfusion and the posterior associative cortex when the time elapsed since last vaccine injection was investigated.</p><p>Conclusions</p><p>Brain perfusion SPECT showed a pattern of cortical and subcortical changes in accordance with the MMF-associated cognitive disorder previously described. These results provide a neurobiological substrate for brain dysfunction in aluminum hydroxide adjuvant-induced MMF patients.</p></div

SPM map.

<p>Positive correlation between brain perfusion and Stroop color and word Z-scores with adjustment for age, sex, socio-cultural level and time delay between brain SPECT and neuropsychological testing. Linear regression analysis shows impairment of posterior associative areas. Significant clusters are displayed with T-score values on 2-dimensional axial, coronal and sagittal orientations (glass-brain—left panel) and projected onto a brain rendered 3D MIP (right panel). P-value < 0.005 at the voxel level for clusters ≥ 100 contiguous voxels (corrected for cluster volume). L, left; R, right; A, anterior; P, posterior.</p

SPM map.

<p>Positive correlation between brain perfusion and dichotic listening (left ear words) normalized scores with adjustment for age, sex, socio-cultural level and time delay between brain SPECT and neuropsychological testing. Linear regression analysis shows diffuse impairment of periventricular areas/corpus callosum. Significant clusters are displayed with T-score values on 2-dimensional projections (glass-brain—left panel) and slices of MRI (right panel) templates in axial, coronal and sagittal orientations. <i>P</i>-value < 0.005 at the voxel level for clusters ≥ 100 contiguous voxels (corrected for cluster volume). L, left; R, right; A, anterior; P, posterior.</p