Practice variations in salivary gland imaging and utility of virtual unenhanced dual energy CT images for the detection of major salivary gland stones
Bibliographic record
Abstract
Background There are variations in computed tomography (CT) protocols used for evaluating sialolithiasis, with some protocols including acquisitions before and after IV contrast administration. Dual-energy CT (DECT) can be used to generate virtual unenhanced (VUE) images, potentially precluding the need for an uninfused scan. Purpose In this study, we performed a survey in order to assess variations in the imaging approach for sialolithiasis and evaluated the accuracy of DECT VUE images for the detection of salivary stones. Material and Methods Practice variations were evaluated by an online survey of the membership of the American Society of Neuroradiology. We then identified 28 patients with salivary gland calcifications matched with 28 negative controls that had both an unenhanced and a contrast-enhanced acquisition performed as DECT. A total of 123 major salivary gland calcifications and 85 tonsilloliths were evaluated and the true unenhanced series was used as gold standard. Results The survey revealed substantial variations in CT protocols used for sialolithiasis. On a per-patient basis, DECT VUE had 96.4% sensitivity and 100% specificity. On a per-calcification basis, sensitivity and specificity was 100% for stones > 2 mm but dropped for smaller calcifications. The false-negative cases corresponded to clinically insignificant, intra-glandular parotid calcifications. Inter-reader agreement was excellent (0.9256). Conclusion This study confirms that there are significant variations in CT protocols used for evaluation of sialolithiasis. A single contrast-enhanced DECT acquisition with reconstruction of VUE may represent an attractive, streamlined alternative and enable the elimination of the true unenhanced phase and associated radiation exposure.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".