A comparison of visual and semiquantitative analysis methods for planar cardiac 123I-MIBG scintigraphy in dementia with Lewy bodies
Bibliographic record
Abstract
OBJECTIVES: Cardiac I-MIBG imaging is an established technique for the diagnosis of dementia with Lewy bodies but various analysis methods are reported in the literature. We assessed different methods in the same cohort of patients to inform best practice. PATIENTS AND METHODS: Seventeen patients with dementia with Lewy bodies, 15 with Alzheimer's disease and 16 controls were included. Planar images were acquired 20 min and 4 h after injection. Nine operators produced heart-to-mediastinum ratios (HMRs) using freehand and 6, 7 and 8 cm diameter circular cardiac regions. Interoperator variation was measured using the coefficient of variation. HMR differences between methods were assessed using analysis of variance. Seven raters assessed the images visually. Accuracy was compared using receiver operating characteristic analysis. RESULTS: There were significant differences in HMR between region methods (P=0.006). However, with optimised cut-offs there was no significant difference in accuracy (P=0.2-1.0). The sensitivity was 65-71% and specificity 100% for all HMR methods. Variation was lower with fixed regions than freehand (P<0.001). Visual rating sensitivity and specificity were 65 and 77% on early images and 76 and 71% on delayed images. There was no significant difference in HMR between early and delayed images (P=0.4-0.7) although a greater separation between means was seen on delayed images (0.73 vs. 0.95). CONCLUSION: HMR analysis using a suitable cut-off is more accurate than visual rating. Accuracy is similar for all methods, but freehand regions are more variable and 6 cm circles easiest to place. We recommend calculating HMR using a 6 cm circular cardiac region of interest on delayed images.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".