Evaluation of Imaging Windows for Tau PET Imaging Using <sup>18</sup>F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer’s Disease Patients

Chotipanich, Chanisa; Nivorn, Monchaya; Kunawudhi, Anchisa; Promteangtrong, Chetsadaporn; Boonkawin, Natphimol; Jantarato, Attapon

doi:10.1177/1536012120947582

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Moreover, the tracer has represented outstanding clinical potential for differential diagnosis among Aβ+AD and Aβ+non-AD tauopathies, cortical basal syndrome (CBD), and progressive supranuclear palsy (PSP) [8][9][10][11][12]. However, it has a lower rate of off-target binding and may improve our capabilities for tau detection [13][14][15]. Consequently, the radiotracer has been considered orphan drug designation diagnosis of PSP and CBD from the European Commission and two by the U.S. Food and Drug Administration.…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

et al. 2021

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Background. Some studies have reported the effectiveness of [18F]PI-2620 as an effective tau-binding radiotracer; however, few reports have applied semiquantitative analysis to the tracer. Therefore, this study’s aim was to perform a semiquantitative analysis of [18F]PI-2620 in individuals with normal cognition and patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Methods. Twenty-six cognitively normal (CN) subjects, 7 patients with AD, and 36 patients with MCI were enrolled. A dynamic positron emission tomography (PET) scan was performed 30–75 min postinjection. PET and T1-weighted magnetic resonance imaging scans were coregistered. The standardized uptake value ratio (SUVr) was used for semiquantitative analysis. The P-Mod software was applied to create volumes of interest. The ANOVA and post hoc Tukey HSD were used for statistical analysis. Results. In the AD group, the occipital lobe had a significantly higher mean SUVr ( 1.46 ± 0.57 ) than in the CN and MCI groups. Compared with the CN group, the AD group showed significantly higher mean SUVr in the fusiform gyrus ( 1.06 ± 0.09 vs. 1.49 ± 0.86 ), inferior temporal ( 1.07 ± 0.07 vs. 1.46 ± 0.08 ), parietal lobe, lingual gyrus, and precuneus regions. Similarly, the AD group demonstrated a higher mean SUVr than the MCI group in the precuneus, lingual, inferior temporal, fusiform, supramarginal, orbitofrontal, and superior temporal regions. The remaining observed regions, including the striatum, basal ganglia, thalamus, and white matter, showed a low SUVr across all groups with no statistically significant differences. Conclusion. A significantly higher mean SUVr of [18F]PI-2620 was observed in the AD group; a significant area of the brain in the AD group demonstrated tau protein deposit in concordance with Braak Stages III–V, providing useful information to differentiate AD from CN and MCI. Moreover, the low SUVr in the deep striatum and thalamus could be useful for excluding primary tauopathies.

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Section: Introductionmentioning

confidence: 99%

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

et al. 2021

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“…2a). Indeed, such time-resolved SUVR plots are informative to help determine the optimal acquisition window [96,[131][132][133].) In practice, however, many PET tracers for which static scans are employed for practical reasons do not always exhibit a perfectly stable SUVR signal during (and either side of) the scanning window, a property often exacerbated in regions of high binding [131,133] (Fig.…”

Section: Static Scan Protocolsmentioning

confidence: 99%

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Schwarz

2021

Neurotherapeutics

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Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect—especially with a clear relationship to dose—can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to “de-risk” the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer’s disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

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“…We calculated SUVR values of 10 tau-PET Meta ROIs as features for classification. The ten ROIs included inferior temporal lobe, lingual gyrus, middle temporal lobe, occipital lobe, parietal lobe, hippocampus, parahippocampus, posterior cingu late gyrus, precuneus and fusiform [ 28 ]; (3) the radiomics model. The radiomics features of the above 10 tau-PET Meta ROIs were extracted as features for classification.…”

Section: Methodsmentioning

confidence: 99%

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

et al. 2022

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Objective: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. Methods: In this study, we selected tau-PET scans from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. Results: Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. Conclusions: Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.

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Evaluation of Imaging Windows for Tau PET Imaging Using ¹⁸F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer’s Disease Patients

Cited by 11 publications

References 22 publications

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

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Evaluation of Imaging Windows for Tau PET Imaging Using 18F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer’s Disease Patients

Cited by 11 publications

References 22 publications

The Evaluation of Tau Deposition with [18F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

The Evaluation of Tau Deposition with [18F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

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Product

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Evaluation of Imaging Windows for Tau PET Imaging Using ¹⁸F-PI2620 in Cognitively Normal Individuals, Mild Cognitive Impairment, and Alzheimer’s Disease Patients

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease

The Evaluation of Tau Deposition with [¹⁸F]PI-2620 by Using a Semiquantitative Method in Cognitively Normal Subjects and Patients with Mild Cognitive Impairment and Alzheimer’s Disease