3-phase dual-energy CT scan as a feasible salvage imaging modality for the identification of non-localizing parathyroid adenomas: A prospective study
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it