Characterizing cancer‐associated myosteatosis: anatomic distribution and cancer‐specific variability of low radiodensity muscle
Bibliographic record
Abstract
Abstract Background Low muscle radiodensity on computed tomography (CT) scan, indicative of myosteatosis, is commonly observed in cancer patients and can be associated with poor prognosis. Radiodensity is typically measured at the level of the third lumbar vertebra (L3). It is unknown whether features at L3 reflect a systemic state affecting peripheral muscle groups, whether images used at different levels can be used as a surrogate if L3 images are unavailable, and how radiodensity varies between cancer types. Methods Core and extremity muscle radiodensities were measured in whole body CT images from melanoma patients to evaluate the anatomical distribution of muscle radiodensity measurements. Core muscle radiodensity was measured in 891 patients with different cancer types to study malignancy‐dependent patterns in muscle radiodensity. Results Low muscle radiodensity at L3 (<30 Hounsfield Unit) was associated with a corresponding lower muscle radiodensity in all muscle groups evaluated (P < 0.001). However, muscle radiodensities were lowest in the core muscle groups compared with muscles in the extremities. Muscle radiodensities at T12 closely correlated with measurements taken at L3 (r = 0.920, P < 0.001), but the correlation was weaker with mid‐thigh measurements (r = 0.745, P < 0.001). The distribution of muscle radiodensities varied significantly with cancer type (P = 0.002). Conclusions The uniform distribution of low muscle radiodensity in cancer patients supports the hypothesis that the underlying mechanism for myosteatosis is systemic in nature. The most reliable measurements of muscle radiodensity are taken using images of core muscles. Variations in muscle radiodensity associated with cancer exist, suggesting that cancer‐specific biological drivers are at play.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".