Different Spectral Hounsfield Unit Curve and High-Energy Virtual Monochromatic Image Characteristics of Squamous Cell Carcinoma Compared with Nonossified Thyroid Cartilage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The attenuation of normal nonossified thyroid cartilage can be similar to that of head and neck squamous cell carcinoma on CT. We compared dual-energy CT spectral Hounsfield unit attenuation characteristics of nonossified thyroid cartilage with that of squamous cell carcinoma to determine the optimal virtual monochromatic image reconstruction energy levels for distinguishing tumor from normal nonossified thyroid cartilage. MATERIALS AND METHODS: Dual-energy CT scans from 30 patients with histopathology-proved squamous cell carcinoma at different primary sites (laryngeal and nonlaryngeal) and 10 healthy patients were evaluated. Patients were scanned with a 64-section single-source scanner with fast-kilovolt (peak) switching, and scans were reconstructed at different virtual monochromatic energy levels ranging from 40 to 140 keV. Spectral attenuation curves of tumor and nonossified thyroid cartilage were quantitatively evaluated and compared. Any part of the tumor invading the cartilage, when present, was excluded from ROI analysis to avoid cross-contamination from areas where there could be a mixture of cartilage and invading tumor. RESULTS: Normal nonossified thyroid cartilage had a characteristic, predictable spectral attenuation curve that was different from that of tumors. The greatest difference in attenuation of nonossified cartilage compared with tumor was on virtual monochromatic images of ≥95 keV (P < .0001), with sharp contrast between the relatively high attenuation of nonossified cartilage compared with that of tumor. CONCLUSIONS: Head and neck squamous cell carcinoma has significantly different attenuation on virtual monochromatic images of ≥95 keV, compared with nonossified thyroid cartilage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".