Liver fat and clinical outcomes in individuals with stage I-III colon or rectal cancer
Bibliographic record
Abstract
BACKGROUND: Liver fat accumulation has been associated with impaired colorectal cancer prognosis. Associations may differ for colon and rectal cancer due to different disease mechanisms and dissemination patterns. Here, we investigated associations between liver fat and cancer recurrence, recurrence-free survival (RFS), and overall survival (OS) among 1596 individuals with stage I-III colon or rectal cancer. METHODS: Within a prospective cohort study, we used data from adults recently diagnosed with colon (n = 1080) or rectal (n = 516) cancer. Liver fat was evaluated using routine contrast-enhanced computed tomography (CT)-scans taken at diagnosis. Cox proportional hazards regression analyses adjusted for clinical and lifestyle-related variables were used to obtain hazard ratios (HRs) and 95% confidence intervals (95% CIs). RESULTS: During a median follow-up of 6.4 and 8.8 years, 247 (15%) recurrences (12% for colon and 22% for rectal cancer) and 418 (26%) deaths (25% for colon and 29% for rectal cancer) occurred, respectively. More liver fat was associated with an increased recurrence risk (HRT3vsT1 = 1.60, 95% CI = 1.02 to 2.50), worse RFS (HRT3vsT1 = 1.45, 95% CI = 1.05 to 2.00), and OS (HRT3vsT1 = 1.67, 95% CI = 1.20 to 2.33) among individuals with colon cancer. Liver fat was not associated with recurrence (HRT3vsT1 = 0.70, 95% CI = 0.42 to 1.18), RFS (HRT3vsT1 = 0.87, 95% CI = 0.59 to 1.30), or OS (HRT3vsT1 = 1.15, 95% CI = 0.74 to 1.80) among individuals with rectal cancer. CONCLUSION: More liver fat was associated with poor clinical outcomes in patients with stage I-III colon cancer. Further studies are needed to confirm these findings and explore mechanistic routes linking liver fat to colon cancer prognosis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".