Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementia - Manera, Ana; Dadar, Mahsa; John Van Swieten; Borroni, Barbara; Sanchez-Valle, Raquel; Moreno, Fermin; Laforce, Robert; Graff, Caroline; Synofzik, Matthis; Galimberti, Daniela; Rowe, James; Masellis, Mario; et al. (bioRxiv, 2019)
INTRODUCTION: Brain structural imaging is paramount for the diagnosis of behavioral variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODS: A total of 515 subjects from two different bvFTD databases (training and validation cohorts) were included to perform voxel-wise deformation-based morphometry analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from morphometric differences in isolation and together with bedside cognitive scores. RESULTS: Average ten-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In a separate validation cohort of genetically confirmed bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added cognitive scores. DISCUSSION: The random forest classifier developed can accurately predict bvFTD at the individual subject level.