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

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-florz...

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Published in:iScience 2023-08, Vol.26 (8), p.107426-107426, Article 107426
Main Authors: Lu, Jiaying, Clement, Christoph, Hong, Jimin, Wang, Min, Li, Xinyi, Cavinato, Lara, Yen, Tzu-Chen, Jiao, Fangyang, Wu, Ping, Wu, Jianjun, Ge, Jingjie, Sun, Yimin, Brendel, Matthias, Lopes, Leonor, Rominger, Axel, Wang, Jian, Liu, Fengtao, Zuo, Chuantao, Guan, Yihui, Zhao, Qianhua, Shi, Kuangyu
Format: Article
Language:English
Subjects:
Clinical neuroscience
Health informatics
Medical imaging
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Summary: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. [Display omitted] •A normalization-free deep-learning (NFDL) model was designed for 18F-florzolotau PET•NFDL is promising for early and accurate differentiation of atypical parkinsonism•Regions driving the model’s decision were aligned with disease-specific topographies•The high generalizability makes NFDL a potential tool for tau PET interpretation Health informatics; Medical imaging; Clinical neuroscience
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2023.107426