Sex-specific prognostic value of automated epicardial adipose tissue quantification on serial lung cancer screening chest computed tomography
Bibliographic record
Abstract
AIMS: Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remains unclear. This study investigated sex differences in the prognostic utility of serial EAT measurements on low-dose chest computed tomography (CT). METHODS AND RESULTS: We analysed baseline and 2-year changes in EAT volume and density using a validated automated deep-learning algorithm in 24 008 heavy-smoking participants from the National Lung Screening Trial (NLST). Sex-stratified multivariable Cox models, adjusted for CV risk factors, body mass index (BMI), and coronary artery calcium (CAC), assessed associations between EAT and all-cause and CV mortality [median follow-up 12.3 years (IQR: 11.9-12.8), 4668 (19.4%) all-cause deaths, 1083 (4.5%) CV deaths]. Women (n = 9841; 41%) were younger, with fewer CV risk factors, lower BMI, fewer pack-years, and lower CAC than men (all P < 0.001). Baseline EAT was associated with similar all-cause and CV mortality risk in both sexes [max. aHR women: 1.70; 95% confidence interval (CI): 1.13-2.55; men: 1.83; 95% CI: 1.40-2.40, P-interaction = 0.986]. However, 2-year EAT changes predicted CV death only in women (aHR: 1.82; 95% CI: 1.37-2.49; P < 0.001), and showed a stronger association with all-cause mortality in women (aHR: 1.52; 95% CI: 1.31-1.77) than in men (aHR: 1.26; 95% CI: 1.13-1.40; P-interaction = 0.041). CONCLUSION: In this large lung cancer screening cohort, serial EAT changes independently predicted CV mortality in women and were more strongly associated with all-cause mortality in women than in men. These findings support routine EAT quantification on chest CT for improved sex-specific cardiovascular risk stratification.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".