[<sup>18</sup>F]FE‐PE2I PET is a diagnostic tool in dementia with Lewy bodies
Bibliographic record
Abstract
Abstract Aim Dementia with Lewy bodies (DLB) is characterized by motor and non‐motor symptoms. The degeneration of the dopaminergic pathway is a hallmark of DLB; for this reason, we aimed to study a recent dopamine transporter (DAT) positron emission tomography (PET) radioligand as a diagnostic tool for DLB. Methods In this study, we used DAT–PET with the radioligand [ 18 F]FE‐PE2I to distinguish DLB subjects from healthy controls (HCs). We also aimed to analyze how DAT binding correlated with clinical features, amyloid load, measured by PET, and cardiac metaiodobenzylguanidine scintigraphy (MIBG). Results Binding potential ( BP ND ) values of [ 18 F]FE‐PE2I were higher in HCs versus DLB in striatum (1.82 ± 0.34 vs. 1.15 ± 0.34; p < 0.001; 95% Confidence Interval [CI]: 0.40–0.96), putamen (2.2 ± 0.36 vs. 1.41 ± 0.51; p < 0.001; 95% CI: 0.39–1.17), caudate (1.38 ± 0.30 vs. 0.88 ± 0.20; p < 0.001; 95% CI: 0.28–0.70), and substantia nigra (0.49 ± 0.091 vs. 0.42 ± 0.084; p = 0.0437; 95% CI: 0.003 to 0.14). After adjusting for age, substantia nigra did not differ between DLB and HCs ( p : 0.46; 95% CI: −0.049 to 0.11); however, BP ND values between DLB and HC in striatum ( p : <0.001; 95% CI: 0.25–0.85), putamen ( p : 0.0012; 95% CI: 0.31–1.13), and caudate ( p : 0.0027; 95% CI: 0.13–0.55) were still significant. Striatum was the best area to correctly classify DLB subjects versus HC compared to the putamen, caudate, and substantia nigra (area under the curve = 0.95, 0.90, 0.93, and 0.73, respectively; 95 CI: 0.87–1.00, 0.79–1.00, 0.84–1.00, 0.55–0.92, respectively). Subjects with altered MIBG showed lower BP ND compared to subjects with normal MIBG in the putamen. Conclusion Our study showed that [ 18 F]FE‐PE2I PET represents a potential diagnostic tool with high accuracy in discriminating DLB patients versus HC, which is valuable for clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".