Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration.
| Publication Type | Academic Article |
| Authors | Ford J, Sweeney E, Skafida M, Glynn S, Amoashiy M, Lange D, Lin E, Chiang G, Osborne J, Pahlajani S, de Leon M, Ivanidze J |
| Journal | Am J Nucl Med Mol Imaging |
| Volume | 11 |
| Issue | 4 |
| Pagination | 313-326 |
| Date Published | 08/15/2021 |
| ISSN | 2160-8407 |
| 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. |
| PubMed ID | 34513285 |
| PubMed Central ID | PMC8414399 |