Performance and Interpretation of Lung Scintigraphy
Bibliographic record
Abstract
PURPOSE: Although ventilation/perfusion (V/Q) scintigraphy is a widely used imaging test, different options are possible for the acquisition and interpretation of the scan. The aim of this study was to assess current practices regarding the use and interpretation of lung scintigraphy in various clinical indications. PATIENTS AND METHODS: An online survey comprising 25 questions was sent to nuclear medicine departments in Australia, Canada, France, Germany, and United States between 2022 and 2023. A single response per department was consolidated. RESULTS: Four hundred nineteen responses were collected (Australia: 32, Canada: 58, France: 149, Germany: 92, and United States: 88). For acute pulmonary embolism (PE) diagnosis, 82.8% of centers reported using SPECT acquisitions (Australia: 93.3%, Canada: 91.8%, France: 99.2%, Germany: 96.2%, and United States: 32.1%). Among them, SPECT images were combined with a CT scan in 70.5% of centers. A total of 10.6% of centers reported not using ventilation for acute PE diagnosis. SPECT acquisition was used in 97.8% of centers using 99m Tc carbon particles, 97.1% 81m Kr gas, 58.7% 99m Tc-DTPA, and 19.4% 133 Xe gas, respectively. For V/Q SPECT interpretation, the EANM criteria were used in 65.0% of departments. A very wide variety of practices were observed in pregnant women and in COVID-19 patients. SPECT acquisition was widely used in the follow-up of PE and for the screening of chronic thromboembolic pulmonary hypertension (>90% of centers), with inconsistency regarding the interpretation of matched perfusion defects in this setting. CONCLUSIONS: This survey shows the strong adoption of SPECT in the various clinical indications of lung scintigraphy, except in the United States, where planar imaging is still mostly used. The survey also shows variability in interpretation criteria both for PE diagnosis and screening for chronic thromboembolic pulmonary hypertension, highlighting the need for further standardizations of practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".