Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images

• Published in: Journal of Nuclear Medicine Vol. 56; no. 9; pp. 1351 - 1358
• Main Authors: Carbonell, Felix, Zijdenbos, Alex P., Charil, Arnaud, Grand’Maison, Marilyn, Bedell, Barry J.
• Format: Journal Article
• Language: English
• Published: United States Society of Nuclear Medicine 01.09.2015
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Alzheimer's disease; Amyloid beta-Peptides - metabolism; Aniline Compounds - pharmacokinetics; Brain - diagnostic imaging; Brain - metabolism; Classification; Cognition & reasoning; Discriminant analysis; Ethylene Glycols - pharmacokinetics; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Male; Neuropsychology; Nuclear medicine; Positron-Emission Tomography - methods; Radiopharmaceuticals; Reproducibility of Results; Sensitivity and Specificity; Severity of Illness Index; Tissue Distribution; Tomography; 18F-florbetapir; mild cognitive impairment; Alzheimer disease; PET; β-amyloid
• DOI: 10.2967/jnumed.115.158774
• ISSN: 0161-5505; 1535-5667

Classification 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. 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 (18)F-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. 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. 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.