Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration.

TitleHeuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration.
Publication TypeJournal Article
Year of Publication2021
AuthorsFord JN, Sweeney EM, Skafida M, Glynn S, Amoashiy M, Lange DJ, Lin E, Chiang GC, Osborne JR, Pahlajani S, de Leon MJ, Ivanidze J
JournalAm J Nucl Med Mol Imaging
Volume11
Issue4
Pagination313-326
Date Published2021
ISSN2160-8407
Abstract

Distinguishing frontotemporal lobar degeneration (FTLD) and Alzheimer Disease (AD) on FDG-PET based on qualitative review alone can pose a diagnostic challenge. SPM has been shown to improve diagnostic performance in research settings, but translation to clinical practice has been lacking. Our purpose was to create a heuristic scoring method based on statistical parametric mapping z-scores. We aimed to compare the performance of the scoring method to the initial qualitative read and a machine learning (ML)-based method as benchmarks. FDG-PET/CT or PET/MRI of 65 patients with suspected dementia were processed using SPM software, yielding z-scores from either whole brain (W) or cerebellar (C) normalization relative to a healthy cohort. A non-ML, heuristic scoring system was applied using region counts below a preset z-score cutoff. W z-scores, C z-scores, or WC z-scores (z-scores from both W and C normalization) served as features to build random forest models. The neurological diagnosis was used as the gold standard. The sensitivity of the non-ML scoring system and the random forest models to detect AD was higher than the initial qualitative read of the standard FDG-PET [0.89-1.00 vs. 0.22 (95% CI, 0-0.33)]. A categorical random forest model to distinguish AD, FTLD, and normal cases had similar accuracy than the non-ML scoring model (0.63 vs. 0.61). Our non-ML-based scoring system of SPM z-scores approximated the diagnostic performance of a ML-based method and demonstrated higher sensitivity in the detection of AD compared to qualitative reads. This approach may improve the diagnostic performance.

Alternate JournalAm J Nucl Med Mol Imaging
PubMed ID34513285
PubMed Central IDPMC8414399
Grant ListP30 AG066512 / AG / NIA NIH HHS / United States
R01 AG068398 / AG / NIA NIH HHS / United States
Related Institute: 
Brain Health Imaging Institute (BHII)

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065