SU‐GG‐I‐158: A Comparison of F‐18 FDG‐PET Imaging in Differential Diagnosis of Alzheimer's Disease with Parkinsonism and Dementia with Lewy Bodies Disease
Bibliographic record
Abstract
Purpose: Dementia with Lewy bodies (DLB) is the second most common cause of degenerative dementia after Alzheimer's disease (AD). The aim of this study was to investigate the diagnostic value of metabolism in the diagnosis of DLB and AD in comparison with [F‐18]fluoro‐2‐deoxy‐D‐glucose(FDG)—PET imaging. Investigations which could help improve the accuracy of discrimination between DLB and AD would be a major advance. Methods: All Subjects underwent 18F‐FDG‐PET and MRI. We compared regional cerebral metabolic images by a voxel‐by‐voxel analysis with statistical parametric mapping (SPM) among DLBD, AD and NC subjects, and evaluated differences of hypometabolic regions. All PET scans be visually rated from equivalent slices, each image volume was registered to match a 18F‐FDG‐PET template in standard MNI (Montreal Neurological Institute) space using statistical parametric mapping. Accurate normalization is essential for quantitative pattern analysis to neurodegenerative disease; the cerebellum or pons was often used as a reference region in the MR image, which assumed no significant regional influence of physiological fluctuations for quantitative. Results: 18F‐FDG‐PET revealed evidence of diffuse hypometabolism in both DLBD and AD with Parkinsonism marked declines in association cortices with relative sparing of subcortical structures and primary somatomotor cortex, a pattern reported previously in AD. Unlike AD, DLBD subjects also had hypometabolism in the occipital association cortex and primary visual cortex. Significant changes in hypometabolism in the volume of interest were assessed in the posterior cingulate gyrus, precuneus and parietal cortices. The severity of the decrease in metabolism in AD patients was significantly greater than in vascular dementia and frontotemporal dementia patients and controls. Conclusion: These findings indicate the presence of diffuse cortical abnormalities in DLBD and suggest that FDG‐PET may be useful in discriminating DLBD from AD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".