Body composition from single versus multi‐slice abdominal computed tomography: Concordance and associations with colorectal cancer survival
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi‐slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. Methods A total of 2871 patients with colorectal cancer diagnosed during 2012–2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid‐L3 cross‐sectional areas and multi‐slice T12–L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi‐slice segmentation platform. To facilitate comparison between single‐slice and multi‐slice measurements, sex‐specific z‐scores were calculated. Pearson correlation coefficients and Bland–Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. Results Single‐slice area and multi‐slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland–Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( 0.42 SD) and SAT ( 0.44 SD), and widest for SKM ( 0.78 SD) and IMAT ( 0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1‐SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72–0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75–0.96). Conclusions Single‐slice L3 areas and multi‐slice T12–L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all‐cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi‐slice tissues to improve individual risk prediction.
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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) | 0.000 | 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 it