Comparison of the ability of different quantitative indices in 123I-FP-CIT single-photon emission computed tomography to differentiate dopaminergic neurodegenerative disease
Bibliographic record
Abstract
Abstract Purpose By imaging dopamine transporter (DAT) uptake in the striatum, 123I-FP-CIT SPECT can differentiate dopaminergic neurodegenerative disease (dNDD) and non-dNDD, which differ in pathophysiology and clinical management. Our aim was to compare and validate the diagnostic abilities of various 123I-FP-CIT SPECT quantitative indices for dNDD. Materials and methods Distribution volume ratio (DVR) and binding ratio (BR), measures of DAT uptake capacity, were measured by analyzing clinical 123I-FP-CIT SPECT images of 29 patients with dNDD, including dementia with Lewy bodies and Parkinson’s disease, and 18 patients with non-dNDD, using Montreal Neurological Institute space-based anatomical standardization and an atlas template, which utilizes statistical parametric mapping. Additionally, we computed the specific binding ratio (SBR) based on Bolt’s method and the maximum and mean standardized uptake values (SUVmax and SUVmean, respectively). Results The caudate-to-occipital lobe, putamen-to-occipital lobe, and striatum-to-occipital lobe ratios (COR, POR, and SOR, respectively) on DVR and POR and SOR on BR were significantly lower in dNDD than in non-dNDD, with areas under the ROC curve (AUCs) of 0.941–0.960, showing high diagnostic accuracy for dNDD. However, the AUC of COR on BR was 0.839, indicating lower diagnostic performance. SBR had an AUC of 0.921, while SUVmax and SUVmean had AUCs of 0.906 and 0.900, respectively. Although striatal asymmetry on both DVR and BR exhibited AUCs of 0.728 and 0.734 and asymmetry on SBR showed an AUC of 0.757, the ratio-based DAT quantitative indices were superior. There were strong positive correlations of DVR with BR, DVR with SBR or SUVmax, BR with SBR or SUVmax, and SBR with SUVmax. Conclusion COR, POR, and SOR on DVR and POR and SOR on BR were the most useful DAT quantitative indices. These indices can be compared with SBR and SUV, suggesting that comprehensive evaluation improves the diagnostic accuracy of dNDD.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".