Abstract 12183: Multi-Center Evaluation of Inter-Site Variability in SPECT Myocardial Blood Flow Measurement
Bibliographic record
Abstract
Introduction: Myocardial blood flow (MBF) imaging with SPECT is possible using cardiac-specific cameras. Single-center studies have shown that SPECT MBF is accurate compared with PET MBF and angiography. Clinical implementation requires consistency of SPECT MBF measurements across multiple sites. This study is the first multi-center evaluation of the inter-site processing variability of SPECT MBF. Objective: To compare SPECT MBF obtained locally to MBF calculated at the core lab in Ottawa, Canada. Methods: Six sites (2 in Canada and one each from Japan, Singapore, Italy, and Germany) each acquired 25 rest/stress MBF studies using Tc99m-tetrofosmin (377 MBq at rest; 1060 MBq at stress) on a pinhole-collimated cadmium-zinc-telluride(CZT)-based cardiac SPECT camera. Patients had intermediate to high pre-test probability of coronary artery disease. MBF was analyzed locally and at a core lab using commercially available software. The time that a room was occupied for a MBF study was compared to that for a standard rest/stress myocardial perfusion study. Results: Preliminary results from 4 sites showed a correlation in MBF between the core lab and the local site of an average of 0.96 (range 0.95 to 0.97) at rest and 0.89 (range 0.77 to 0.96) at stress. From Bland-Altman analysis, the mean bias from zero in global MBF between local and core-lab analysis was 3.3% +/- 13.4% at rest and 3.0 % +/- 18.6% at stress. Between the 4 sites, the bias ranged from -4.1% to 9.9% at rest and from -6.3% to 12.8% at stress; the standard deviation (SD) ranged from 10.5% to 14.0% at rest and from 12.0% to 24.5% at stress. There was no significant difference between sites in the SD. The SD of the local-to-core-lab MBF differences is less than the SD of the test-retest difference of 30%, measured in a previous single-center study. The average additional time to perform a MBF study acquisition varied between sites from 45 to 76 min. This additional time includes the time needed to pharmacologically stress the patient which is normally done outside the camera room. Conclusions: This multi-center evaluation supports the feasibility of consistently measuring MBF using CZT-based cardiac SPECT cameras, as has been suggested from previous single-center studies.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".