Impact of visceral fat volume and fat density on biochemical outcome after radical prostatectomy and postoperative radiotherapy
Bibliographic record
Abstract
BACKGROUND: To assess the predictive value of visceral adipose tissue (VAT) and adipose tissue density after both radical prostatectomy (RP) and adjuvant or salvage external beam radiotherapy (EBRT). MATERIALS AND METHODS: We randomly selected 201 patients treated with RP and EBRT between 2005 and 2015. Visceral adipose tissue and subcutaneous adipose tissue volumes were manually contoured and corresponding tissue densities in Hounsfield units (HU) calculated. Time to biochemical recurrence (BCR) was calculated using the Kaplan-Meier method and comparisons were made using the log-rank test. Cox regression analysis was done for multivariate analysis. RESULTS: Median time to BCR or last follow-up was 32 months. In univariate analysis for BCR, VAT volume and fat density were both associated with a better outcome (p=0.025 and p=0.024, respectively) as well as seminal vesicle involvement (p=0.024). Body mass index (BMI) was not predictive of BCR (p=0.32). In a multivariate model including seminal vesicle involvement, both a VAT volume above the median (HR2.5, 95%CI 1.1-5.7, p=0.03) and a VAT density (HR 2.4, 95%CI 1.1-5.1, p=0.028) above the median remained predictive for a better biochemical outcome. Adjusting for BMI did not significantly change the model. CONCLUSIONS: In both univariate and multivariate analysis, patients with both a larger VAT volume and density had a better biochemical outcome. The interaction between prostate cancer aggressiveness and visceral fat volume and density needs to be further evaluated to provide a better understanding of this disease.
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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 | 0.003 |
| 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) | 0.002 | 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".