Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort

International audienceBackgroundMRI computational tools represent promising instruments to improve the diagnostic accuracy of dementias including Alzheimer’s disease (AD). The Automated Segmentation Softwares (ASS) measure various regions of interest (ROI) volumes, thus supporting physicians in clinical decision-making processes. However, their accuracy has mainly been evaluated for research. Recent developments in Support Vector Machine (SVM) classifier might increase the diagnostic value of MRI indexes compared with ASS. Our objective was to investigate the classification rate based on two-class classifiers.MethodsOur study evaluated two ASS (VolBrain and NeuroReader) and two SVM approaches in a monocentric memory center routine clinical cohort of 263 patients with various dementia etiologies (Early and Late-Onset AD (EOAD, LOAD), Cortico-Basal Degeneration, Lewy Body Dementia, Fronto-Temporal Dementia (FTD), Logopenic Variant of Primary Progressive Aphasia and Semantic Dementia) and depression. All patients had a routine MRI at 1, 1.5 or 3 Tesla. We first entered all ROI volumes from ASS in a univariate analysis. Then, we entered volumes obtained from each ASS separately in an SVM classifier. Finally, results where compared to a classifier based on whole brain gray matter (GM) segmentation maps using SPM12.ResultsIn the univariate classification paradigm, the diagnostic accuracy ranged from 50% to 70%, Frontal and Temporal Lobe providing the most accurate scores and hippocampal volumetry only distinguishing LOAD and EOAD from FTD with a respectively 50% and 60% accuracy. SVM classification provided similar accuracy for both ASS ranging from 60 to 80%. Nonetheless, classification using whole brain GM improved the accuracies ranging from 65 to 85% (FTD vs EAOD: 82%, EOAD vs Depression: 83%, FTD vs Depression: 82%).ConclusionsNovel computational tools can be useful in clinical practice and provide comprehensive information supporting clinicians in decision-making processes. ASS analyzed in a univariate way was moderately adequate, with poor accuracy compared with its implementation in an SVM classifier. SVM using whole brain segmentation yielded the highest diagnostic accuracies. Furthermore, SVM performed as well as published accuracies of pathophysiological markers of AD to distinguish this etiology from other dementias and depression. Implementation of whole brain SVM classification in clinical routine could represent a valuable diagnostic tool