A Comparison of Two Statistical Mapping Tools for Automated Brain FDG-PET Analysis in Predicting Conversion to Alzheimer’s Disease in Subjects with Mild Cognitive Impairment

Title:A Comparison of Two Statistical Mapping Tools for Automated Brain FDG-PET Analysis in Predicting Conversion to Alzheimer’s Disease in Subjects with Mild Cognitive Impairment

Volume: 17 Issue: 13

Author(s): Valentina Garibotto*, Sara Trombella, Luigi Antelmi, Paolo Bosco, Alberto Redolfi, Claire Tabouret-Viaud, Olivier Rager, Gabriel Gold, Panteleimon Giannakopoulos, Silvia Morbelli, Flavio Nobili, Robert Perneczky, Mira Didic, Eric Guedj, Alexander Drzezga, Rik Ossenkoppele, Bart Van Berckel, Osman Ratib and Giovanni B. Frisoni

Affiliation:

• Laboratory of Neuroimaging and Innovative Molecular Tracer, University of Geneva, Geneva,Switzerland

Keywords: FDG-PET, Alzheimer's disease, MCI, Automated analysis, tatistical parametric mapping, hypometabolic pattern.

Abstract:

Objective: Automated voxel-based analysis methods are used to detect cortical hypometabolism typical of Alzheimer’s Disease (AD) on FDG-PET brain scans. We compared the accuracy of two clinically validated tools for their ability to identify those MCI subjects progressing to AD at followup, to evaluate the impact of the analysis method on FDG-PET diagnostic performance.

Methods: SPMGrid and BRASS (Hermes Medical Solutions, Stockholm, Sweden) were tested on 131 MCI and elderly healthy controls from the EADC PET dataset. The concordance between the tools was tested by correlating the quantitative parameters (z- and t-values), calculated by the two software tools, and by measuring the topographical overlap of the abnormal regions (Dice score). Three independent expert readers blindly assigned a diagnosis based on the two map sets. We used conversion to AD dementia as the gold standard.

Results: The t-map and z-map calculated with SPMGrid and BRASS, respectively, showed a good correlation (R > .50) for the majority of individual cases (128/131) and for the majority of selected regions of interest (ROIs) (98/116). The overlap of the hypometabolic patterns from the two tools was, however, poor (Dice score .36). The diagnostic performance was comparable, with BRASS showing significantly higher sensitivity (.82 versus .59) and SPMGrid showing higher specificity (.87 versus .52).

Conclusion: Despite similar diagnostic performance in predicting conversion to AD in MCI subjects, the two tools showed significant differences, and the maps provided by the tools showed limited overlap. These results underline the urgency for standardization across FDG-PET analysis methods for their use in clinical practice.

Cite this article as:

Garibotto Valentina*, Trombella Sara , Antelmi Luigi, Bosco Paolo , Redolfi Alberto, Tabouret-Viaud Claire , Rager Olivier, Gold Gabriel , Giannakopoulos Panteleimon , Morbelli Silvia , Nobili Flavio , Perneczky Robert , Didic Mira , Guedj Eric , Drzezga Alexander , Ossenkoppele Rik , Berckel Van Bart , Ratib Osman and Frisoni B. Giovanni , A Comparison of Two Statistical Mapping Tools for Automated Brain FDG-PET Analysis in Predicting Conversion to Alzheimer’s Disease in Subjects with Mild Cognitive Impairment, Current Alzheimer Research 2020; 17 (13) . https://dx.doi.org/10.2174/1567205018666210212162443