3-phase dual-energy CT scan as a feasible salvage imaging modality for the identification of non-localizing parathyroid adenomas: A prospective study
Bibliographic record
Abstract
OBJECTIVES: Accurate pre-operative imaging of parathyroid adenomas (PAs) is essential for successful minimally invasive surgery; however, rates of non-localizing PAs can be as high as 18 %. Multiphasic dual-energy CT (DECT) has the potential to increase accuracy of PA detection by enabling creation of paired material maps and spectral tissue characterization. This study prospectively evaluated the utility of 3-phase DECT for PA identification in patients with failed localizatio n via standard imaging. METHODS: Patients with primary hyperparathyroidism and non-localizing PAs underwent a 3 phase post-contrast DECT scan acquired at 25, 55, and 85 s. The scans were prospectively evaluated by two head and neck radiologists. Pre-operative localization was compared to intraoperative localization and final histopathology. A post-hoc DECT spectral density characterization was performed on pathologically-proven PAs. RESULTS: Out of 29 patients with primary hyperparathyroidism and non-localized PAs, DECT identified candidates in 26. Of the 23 patients who underwent parathyroidectomy, DECT provided precise anatomic localization in 20 patients (PPV = 87.0%), one with multi-gland disease. The virtual unenhanced images were not found to be useful for diagnosis but successful diagnosis was made without an unenhanced phase regardless. Spectral analysis demonstrated a distinct spectral Hounsfield attenuation curve for PAs compared to lymph nodes on arterial phase images. CONCLUSION: 3-phase DECT without an unenhanced phase is a feasible salvage imaging modality for previously non-localizing parathyroid adenomas. Optimal interpretation is achieved based on a combination of perfusion characteristics and other morphologic features. Advanced spectral DECT analysis has the potential for further increasing accuracy of PA identification in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".