Is Long–Axial-Field-of-View PET/CT Cost-Effective? An International Health–Economic Analysis
Bibliographic record
Abstract
Our aim is to assess the cost-effectiveness of long–axial-field-of-view (LAFOV) versus short–axial FOV (SAFOV) PET/CT systems using international data. Methods: Our model compares equipment and operational costs for a PET/CT center and investigates the effect of camera choice (SAFOV vs. LAFOV) and operational models. Variables include scanner, personnel, radiopharmaceuticals, and operational costs. Economic performance was measured as cost per scan per patient, the total maximum number of scans possible, and the incremental cost-effectiveness ratio. The willingness-to-pay threshold (WTPT) was taken as the cost of a PET/CT scan using the baseline scenario. Radiopharmaceutical requirements, radiation dose to staff and patients, and patient time were modeled. Results: An LAFOV system can examine as many patients per day (n = 36) as 2 SAFOV systems but requires fewer technologists (4.5 LAFOV vs. 6.8 SAFOV full-time equivalents) and lower activity (12.5 vs. 35.6 GBq/d), resulting in lower personnel doses (0.9 vs. 2.0 mSv/y). For all countries, LAFOV resulted in lowest per-patient scan costs. The most cost-ineffective method was the use of extended hours. Incremental cost-effectiveness ratio analysis strongly favored LAFOV for all countries, including low-income economies, with WTPT met for all jurisdictions. Net monetary benefit was highest for LAFOV. The minimum number of patients needed to meet WTPT for LAFOV was lowest in lower-income countries, suggesting that high throughput or high per-procedure income is not a prerequisite for cost-effective LAFOV usage. Conclusion: LAFOV was shown to facilitate higher patient throughput at lower per-patient and total lifetime operational costs and with lower radiopharmaceutical requirements. These data suggest that LAFOV systems are not just suited to well-resourced academic centers but also are an economically attractive solution for community and resource-limited settings.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".