Open AccessCerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach
Author(s) -
Paul Blanc-Durand,
Axel Van Der Gucht,
Éric Guedj,
Mukedaisi Abulizi,
Mehdi Aoun-Sebaïti,
Lionel Lerman,
Antoine Verger,
FrançoisJérôme Authier,
Emmanuel Itti
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0181152
Subject(s) - statistical parametric mapping , support vector machine , medicine , population , artificial intelligence , pattern recognition (psychology) , magnetic resonance imaging , nuclear medicine , pathology , computer science , radiology , environmental health
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 18 F-FDG brain profiles. Methods 18 F-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 18 F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.