A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care
Bibliographic record
Abstract
Human body composition is important in numerous cancer research domains. Our objective was to evaluate clinically accessible methods to achieve practical and precise measures of body composition in cancer patients. Dual-energy X-ray absorptiometry (DXA)-based analysis of fat and fat-free mass was performed in 50 cancer patients and compared with bioelectrical impedance analysis (BIA) and with regional computed tomography (CT) images available in the patients' medical records. BIA overestimated or underestimated fat-free mass substantially compared with DXA as the method of reference (up to 9.3 kg difference). Significant changes in fat-free mass over time detected with DXA in a subset of 21 patients (+2.2 +/- 3.2%/100 days, p = 0.003), was beyond the limits of detection of BIA. Regional analysis of fat and fat-free tissue at the 3rd lumbar vertebra with either DXA or CT strongly predicted whole-body fat and fat-free mass (r = 0.86-0.94; p < 0.001). CT images provided detail on specific muscles, adipose tissues and organs, not provided by DXA or BIA. CT presents great practical significance due to the prevalence of these images in patient diagnosis and follow-up, thus marrying clinical accessibility with high precision to quantify specific tissues and to predict whole-body composition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".