Clinical Evaluation of 18F-PI-2620 as a Potent PET Radiotracer Imaging Tau Protein in Alzheimer Disease and Other Neurodegenerative Diseases Compared With 18F-THK-5351
Bibliographic record
Abstract
PURPOSE: PET is a useful tool for detecting the presence and extent of brain tau accumulation. However, most first-generation tau PET tracers are limited for high off-target binding and detection of tau in non-Alzheimer disease (AD). This study evaluated potential clinical applications of F-PI-2620 as a novel PET tracer with a high binding affinity for tau deposition in AD and non-AD tauopathies. METHODS: Twenty-six participants diagnosed with either mild cognitive impairment, probable AD, frontotemporal dementia, or parkinsonism, as well as healthy controls underwent a 60- to 90-minute brain PET scan after 7 mci (259 MBq) injection of F-PI-2620. Some participants had previous PET scans using F-THK-5351 or F-FP-CIT for dopamine transporter imaging. RESULTS: All participants showed no increase in off-target binding in basal ganglia on F-PI-2620 PET images, as noted for first-generation tau tracers. Aβ+ mild cognitive impairment or AD patients showed diverse cortical F-PI-2620 uptake in frontotemporoparietal cortex that correlated with Mini-Mental Status Examination (ρ = -0.692, P = 0.013). Aβ+ Parkinson disease with dementia and (Aβ unknown) primary progressive aphasia patients also showed increased F-PI-2620 uptakes in the frontotemporoparietal cortex. Patients with parkinsonism showed increased uptakes in the pallidum compared with Aβ- healthy controls (left: 1.41 ± 0.14 vs 1.04 ± 0.13, P = 0.014; right: 1.18 ± 0.16 vs 0.95 ± 0.07, P = 0.014). CONCLUSIONS: F-PI-2620 PET might be a sensitive tool to detect cortical tau deposits in patients with Aβ+ AD and Aβ+ non-AD tauopathies. Furthermore, this study showed that "off-target" binding in the basal ganglia does not affect F-PI-2620.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".