Computed tomography-derived assessments of regional muscle volume: Validating their use as predictors of whole body muscle volume in cancer patients
Bibliographic record
Abstract
Objective: Evaluate the accuracy of CT-derived regional skeletal muscle volume (SMV) measurements to predict whole body SMV in patients with melanoma. Methods: 148 patients with advanced melanoma who underwent whole body positron emission tomography/CT were studied. Whole body SMV was measured on CT and used as the reference standard. CT-derived regional measures of SMV were obtained in the thorax, abdomen, pelvis, and lower limbs. Models were developed on a discovery cohort (n-98), using linear regression to model whole body SMV as a function of each regional measure, and clinical factors. Predictive performance of the derived models was evaluated in a validation cohort (n = 50) by estimating the explained variation (R 2) of each model. Results: In the discovery cohort, all regional SMV measurements were significantly associated with whole body SMV [β1 range: 0.673–1.153, all p < 0.001)]. The magnitude of association was greatest for pelvic regional measurements {β = 1.153, [95% confidence interval (0.989, 1.317)]}. Prediction algorithms incorporating clinical variables and regional SMVs were developed to estimate whole body SMV from regional assessments. Using the validation cohort to predict whole body SMV, the R 2 values for the pelvic, abdominal and thoracic regional measurements were 0.89, 0.86, 0.78. Conclusion: Regional measures of SMV are strong predictors of whole body SMV in patients with advanced melanoma. Advances in knowledge: The first study utilizing whole body imaging as a reference standard validating the use of regional SMVs in cancer patients, including validating the use of regional SMVs outside of traditionally assessed areas.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".