Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier

for the Progressive Supranuclear Palsy Neuroimage Initiative (PSPNI)

Research output: Contribution to journalJournal Article peer-review

2 Scopus citations

Abstract

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

Original languageEnglish
Article number107426
Pages (from-to)107426
JournaliScience
Volume26
Issue number8
DOIs
StatePublished - 18 08 2023
Externally publishedYes

Bibliographical note

© 2023 The Author(s).

Keywords

  • Clinical neuroscience
  • Health informatics
  • Medical imaging

Fingerprint

Dive into the research topics of 'Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier'. Together they form a unique fingerprint.

Cite this