Skeletal Muscle Radiation Attenuation at C3 Predicts Survival in Head and Neck Cancer
Bibliographic record
Abstract
Background: Sarcopenia assessed by skeletal muscle area (SMA) at the third lumbar vertebra (L3) is an established prognostic marker in many malignancies, including head and neck cancer (HNC). However, in HNC, L3 is rarely assessed. The prognostic value of myosteatosis, measured by skeletal muscle radiation attenuation (SMRA) remains largely unexplored. This study evaluated both muscle metrics at the third cervical vertebra (C3) for locoregional control (LRC) and overall survival (OS) in HNC. Methods: SMA and SMRA at C3 were quantified in CT scans of 904 HNC cases by a deep learning-based segmentation pipeline with manual verification. Cox proportional hazards models assessed associations with LRC and OS. Results: Median SMA was 36.64 cm2 (IQR: 30.12–42.44). Median SMRA was 50.77 HU (IQR: 43.04–57.39). In multivariable analysis, lower SMA (HR 1.85, 95% CI: 1.19–2.88, p ≤ 0.001) and lower SMRA (HR 1.76, 95% CI: 1.22–2.54, p < 0.001) were associated with lower LRC. For OS, lower SMA (HR 1.53, 95% CI:1.06–2.20, p = 0.02) and lower SMRA (HR 2.13, 95% CI: 1.58–2.88, p < 0.001) were associated with a worse outcome in multivariable analysis. Conclusions: Both SMRA and SMA assessed at C3 correlate with worse LRC and OS in HNC.
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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".