Abdominal adipose tissue radiodensity is associated with survival after colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Adipose tissue radiodensity may have prognostic importance for colorectal cancer (CRC) survival. Lower radiodensity is indicative of larger adipocytes, while higher radiodensity may represent adipocyte atrophy, inflammation, or edema. OBJECTIVES: We investigated associations of adipose tissue radiodensity and longitudinal changes in adipose tissue radiodensity with mortality among patients with nonmetastatic CRC. METHODS: In 3023 patients with stage I-III CRC, radiodensities of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were quantified from diagnostic computed tomography (CT) images. There were 1775 patients with follow-up images available. Cox proportional hazards models and restricted cubic splines were used to examine associations of at-diagnosis values and of longitudinal changes in VAT and SAT radiodensities with risks of death after adjusting for potential confounders, including body size and comorbidities. RESULTS: VAT and SAT radiodensities were linearly associated with all-cause mortality: the HRs for death per SD increase were 1.21 (95% CI, 1.11-1.32) for VAT radiodensity and 1.18 (95% CI, 1.11-1.26) for SAT radiodensity. Changes in adipose tissue radiodensity had curvilinear associations with risks of death. The HR for an increase in VAT radiodensity of at least 1 SD was 1.53 (95% CI, 1.23-1.90), while the HR for a decrease of at least 1 SD was nonsignificant at 1.11 (95% CI, 0.84-1.47) compared with maintaining radiodensity within 1 SD of baseline. Similarly, increases (HR, 1.88; 95% CI, 1.48-2.40) but not decreases (HR, 1.20; 95% CI, 0.94-1.54) in SAT radiodensity significantly increased the risk of death compared with no change in radiodensity. CONCLUSIONS: In patients with nonmetastatic CRC, adipose tissue radiodensity is a novel risk factor for total mortality that is independent of BMI and changes in body weight.
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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 | 0.000 | 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 | 0.001 |
| 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".