Low-Energy Virtual Monochromatic Dual-Energy Computed Tomography Images for the Evaluation of Head and Neck Squamous Cell Carcinoma: A Study of Tumor Visibility Compared With Single-Energy Computed Tomography and User Acceptance
Bibliographic record
Abstract
OBJECTIVE: Dual-energy computed tomography (CT) 40-keV virtual monochromatic images (VMIs) have been reported to improve visualization of head and neck squamous cell carcinoma, but a direct comparison to single-energy CT (SECT) is lacking, and there is debate regarding subjective user preference. We compared 40-keV VMIs with SECT and performed a subjective evaluation of their utility and acceptability for clinical use. METHODS: A total of 60 dual-energy CT and 60 SECT scans from 2 different institutions were evaluated. Tumor conspicuity was evaluated objectively using absolute and relative attenuation and subjectively by 3 head and neck specialists and 3 general radiologists. RESULTS: Tumors had significantly higher absolute and relative attenuation on 40-keV VMIs (P < 0.0001). Subjectively, the 40-keV VMIs improved visualization, with substantial (κ, 0.61-0.80) to almost perfect (κ, 0.81-1) interrater agreements. CONCLUSIONS: The 40-keV VMIs improve tumor visibility objectively and subjectively both by head and neck specialists and general radiologists.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".