Cost-Effectiveness of Positron Emission Tomography/Computed Tomography (PET/CT) in the Initial N-Staging of Head–Neck Cancer and Comparison with CT and Magnetic Resonance Imaging (MRI)
Bibliographic record
Abstract
The aim of the study was to evaluate the cost-effectiveness of PET/CT in the initial N-staging of head-neck cancer (HNC) and to compare it with alternative strategies using CT or MRI within the Greek National Healthcare System. A cohort of 100 clinically N0 (with no apparent metastatic cervical lymph nodes) HNC patients was simulated over a 10-year time horizon. Initially, a decision tree model was used to simulate the following three different imaging strategies for HNC staging: (a) whole-body FDG-PET/CT, (b) CT of the neck, chest, and abdomen ("CT"), and (c) MRI of the neck plus CT of the chest-abdomen ("MRI"). Subsequently, a Markov model was used to simulate transitions into the health states of recurrence and death. Epidemiological evidence, diagnostic accuracy rates, transition probabilities, and healthcare costs were obtained from the literature and official local tariffs. The estimated total costs per patient were EUR 128,729 for PET/CT, EUR 128,779 for MRI, and EUR 128,585 for CT. The corresponding life years (LYs) were 6.171 LYs for PET/CT, 6.170 LYs for MRI, and 6.170 LYs for CT, respectively. The analysis showed that PET/CT dominates MRI. The incremental cost-effectiveness ratio (ICER) of PET/CT vs. CT was estimated at EUR 144,984 per LY gained. All three imaging strategies had comparable health outcomes and costs, with PET/CT being an appropriate and efficient imaging modality because of its high diagnostic accuracy in the N-staging of HNC.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".