Optimal target region for subject classification on the basis of amyloid PET images

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dc.contributor.authorCarbonell, Fen_AU
dc.contributor.authorZijdenbos, APen_AU
dc.contributor.authorCharil, Aen_AU
dc.contributor.authorGrand’Maison, MGen_AU
dc.contributor.authorBedell, BJen_AU
dc.date.accessioned2017-05-16T00:13:47Zen_AU
dc.date.available2017-05-16T00:13:47Zen_AU
dc.date.issued2015-09-01en_AU
dc.date.statistics2017-05-16en_AU
dc.description.abstractClassification of subjects on the basis of amyloid PET scans is increasingly being used in research studies and clinical practice. Although qualitative, visual assessment is currently the gold standard approach, automated classification techniques are inherently more reproducible and efficient. The objective of this work was to develop a statistical approach for the automated classification of subjects with different levels of cognitive impairment into a group with low amyloid levels (AβL) and a group with high amyloid levels (AβH) through the use of amyloid PET data from the Alzheimer Disease Neuroimaging Initiative study. Methods: In our framework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operating characteristic approach that optimizes the selection of a region of interest (ROI) and a cutoff value for the automated classification of subjects into the AβL and AβH groups. The robustness, spatial stability, and generalization of the resulting target ROIs were evaluated by use of the standardized uptake value ratio for 18F-florbetapir PET images from subjects who served as healthy controls, subjects who had mild cognitive impairment, and subjects who had Alzheimer disease and were participating in the Alzheimer Disease Neuroimaging Initiative study. Results: We determined that several iterations of the discriminant analysis improved the classification of subjects into the AβL and AβH groups. We found that an ROI consisting of the posterior cingulate cortex/precuneus and the medial frontal cortex yielded optimal group separation and showed good stability across different reference regions and cognitive cohorts. A key step in this process was the automated determination of the cutoff value for group separation, which was dependent on the reference region used for the standardized uptake value ratio calculation and which was shown to have a relatively narrow range across subject groups. Conclusion: We developed a data-driven approach for the determination of an optimal target ROI and an associated cutoff value for the separation of subjects into the AβL and AβH groups. Future work should include the application of this process to other datasets to facilitate the determination of the translatability of the optimal ROI obtained in this study to other populations. Ideally, the accuracy of our target ROI and cutoff value could be further validated with PET-autopsy data from large-scale studies. It is anticipated that this approach will be extremely useful for the enrichment of study populations in clinical trials involving putative disease-modifying therapeutic agents for Alzheimer disease. © 2015, by the Society of Nuclear Medicine and Molecular Imaging, Inc.en_AU
dc.identifier.citationCarbonell, F., Zijdenbos, A. P., Charil, A., Grand'Maison, M., & Bedell, B. J. (2015). Optimal target region for subject classification on the basis of amyloid PET images. Journal of Nuclear Medicine, 56(9), 1351-1358. doi:10.2967/jnumed.115.158774en_AU
dc.identifier.govdoc8228en_AU
dc.identifier.issn2159-662Xen_AU
dc.identifier.issue9en_AU
dc.identifier.journaltitleJournal of Nuclear Medicineen_AU
dc.identifier.pagination1351-1358en_AU
dc.identifier.urihttp://dx.doi.org/10.2967/jnumed.115.158774en_AU
dc.identifier.urihttp://apo.ansto.gov.au/dspace/handle/10238/8702en_AU
dc.identifier.volume56en_AU
dc.language.isoenen_AU
dc.publisherSociety of Nuclear Medicine and Molecular Imagingen_AU
dc.subjectClassificationen_AU
dc.subjectPositron computed tomographyen_AU
dc.subjectClinical trialsen_AU
dc.subjectNeurologyen_AU
dc.subjectCerebral cortexen_AU
dc.titleOptimal target region for subject classification on the basis of amyloid PET imagesen_AU
dc.typeJournal Articleen_AU
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