Visual reading for [18F]Florzolotau ([18F]APN-1607) tau PET imaging in clinical assessment of Alzheimer's disease

A Peculiar Tau Accumulation Pattern Identified Via 18 F-Florzolotau PET Imaging in a Patient With Frontotemporal Dementia Caused by a Mutation in the MAPT Gene

ABSTRACT

We reported imaging findings with a peculiar tau accumulation pattern in a 56 year-old woman with frontotemporal dementia caused by Q351R mutation in the microtubule-associated protein tau ( MAPT ) gene. She had a 10-year history of gradually worsening memory loss. 18 F-FDG PET demonstrated hypometabolism in medial temporal lobes, but 18 F-florbetapir PET manifested no abnormal amyloid beta deposition, which ruled out Alzheimer disease. 18 F-florzolotau PET showed tau proteins accumulated in medial temporal lobes, basal ganglia brainstem, and cerebellum. The pattern is different from other known MAPT gene mutation. Multitracer imaging can help differentiate between Alzheimer disease and frontotemporal dementia caused by MAPT mutation.

PET brain imaging in neurological disorders

Brain disorders are a series of conditions with damage or loss of neurons, such as Parkinson's disease (PD), Alzheimer's disease (AD), or drug dependence. These individuals have gradual deterioration of cognitive, motor, and other central nervous system functions affected. This degenerative trajectory is intricately associated with dysregulations in neurotransmitter systems. Positron Emission Tomography (PET) imaging, employing radiopharmaceuticals and molecular imaging techniques, emerges as a crucial tool for detecting brain biomarkers. It offers invaluable insights for early diagnosis and distinguishing brain disorders. This article comprehensively reviews the application and progress of conventional and novel PET imaging agents in diagnosing brain disorders. Furthermore, it conducts a thorough analysis on merits and limitations. The article also provides a forward-looking perspective in the future development directions of PET imaging agents for diagnosing brain disorders and proposes potential innovative strategies. It aims to furnish clinicians and researchers with an all-encompassing overview of the latest advancements and forthcoming trends in the utilization of PET imaging for diagnosing brain disorders.

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network

OBJECTIVE

The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions.

METHODS

The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD.

RESULTS

The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001.

INTERPRETATION

The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

Identifying Mixed Dementia With Lewy Bodies and Alzheimer Disease Using Multitracer PET Imaging: A Case Study

ABSTRACT

We reported imaging findings with complex signs that were corresponded with both dementia with Lewy bodies (DLB) and Alzheimer disease (AD) in the case of a 78-year-old woman. Initially suspected as DLB due to cognitive and movement issues, diagnostic support included the cingulate island sign on 18 F-FDG PET, positive 131 I-MIBG cardiac scintigraphy, and DAT PET. However, MRI indicated hippocampal atrophy, and 18 F-FDG PET showed hypometabolism in the medial temporal lobe, suggesting the possibility of concomitant AD. Subsequent detection of β-amyloid pathology and tau accumulation in the brain further supported the concurrent presence of AD pathology.

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-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.