Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain...

Clinical trials of the PET amyloid imaging agent (18)F-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls.

Data from 345 consenting subjects enrolled in 8 prior clinical trials of (18)F-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n = 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n = 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group.

Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold.

Categorization of (18)F-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results.