Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth

Abstract

Purpose End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve (AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 (AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

Longitudinal changes in 18F-Flutemetamol amyloid load in cognitively intact APOE4 carriers versus noncarriers: Methodological considerations

“…18 F-Flutemetamol is a radiotracer that has a high affinity for brain amyloid plaques, can discriminate AD cases from controls, and shows high concordance with histopathological neuritic amyloid plaque density ( Reinartz et al, 2022 , Salloway et al, 2017 ). Despite relative concordance of amyloid-PET acquisition and processing, the choice of reference region differs largely between sites, in particular when working cross-sectionally versus longitudinally.…”

Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study

“…Quantification of amyloid burden in “absolute” units can be leveraged into the definition of threshold differentiating stages of amyloid pathology that are comparable across centers and tracers. So far, using mostly cross‐sectional data, various CL thresholds and windows have been established based on histopathology, 30 , 31 , 32 visual read, 19 , 33 , 34 , 35 , 36 , 37 agreement with other amyloid biomarkers, 38 and disease stage. 30 , 39 These thresholds have been established to reflect the earliest signs of the presence of Aβ compared to post‐mortem studies ( 10–12 CL), and compared to visual reads ( 16–26 CL) (summary in Pemberton et al 9 ).…”