Measurement of peri‐prostatic fat thickness using transrectal ultrasonography (TRUS): a new risk factor for prostate cancer
Bibliographic record
Abstract
UNLABELLED: Study Type - Prognosis (cohort) Level of Evidence 2b. What's known on the subject? and What does the study add? ADIPOSE tissue secretes various endocrine and paracrine mediators. Some authors have begun to consider whether peri-prostatic fat (PPF) may interact with the prostate and play a role in carcinogenesis. It has recently been shown that the PPF quantity measured by CT is associated with more aggressive disease in patients undergoing radiation therapy. Our group studied a population not yet diagnosed with prostate cancer. By doing so we were able to identify PPF thickness on transrectal ultrasonography as a risk factor for prostate cancer detection upon biopsy, and as a risk factor for high-grade disease. Our study also raises interesting questions about the underlying mechanisms of the association between PPF quantity and prostate cancer. OBJECTIVE: To determine if the amount of peri-prostatic fat (PPF) on transrectal ultrasonography (TRUS) is a risk factor for incident prostate cancer overall and high-grade prostate cancer (Gleason ≥4). PATIENTS AND METHODS: A prospectively maintained database of patients undergoing prostate biopsy at Princess Margaret Hospital for cancer suspicion was used. • All TRUS examinations were retrospectively reviewed upon 'blinding' to outcome. • PPF thickness, measured as the distance between the prostate and the pubic bone, was used as an index of the quantity of PPF. • PPF measurements, together with other prostate cancer risk factors, were evaluated against prostate cancer and high-grade prostate cancer detection upon biopsy with univariable and multivariable logistic regression and area under the receiver operating characteristic curve (AUC) analysis. RESULTS: Of the 931 patients, 434 (47%) were diagnosed with prostate cancer and 218 (23%) were diagnosed with high-grade prostate cancer. • The mean (range) PPF thickness was 5.3 (0-15) mm. • Increasing PPF thickness was associated with prostate cancer and high-grade prostate cancer diagnosis, with graded effect. When adjusting for other variables, the odds of detecting any prostate cancer and high-grade prostate cancer increased 12% (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.02-1.23) and 20% (OR 1.20, 95% CI 1.07-1.34), respectively, for each millimetre increase in PPF thickness. • The AUCs for the association of PPF with prostate cancer and high-grade prostate cancer were 0.58 (95% CI 0.54-0.62) and 0.59 (95% CI 0.55-0.64), respectively. CONCLUSION: The amount of PPF can be estimated with TRUS and is a predictor of prostate cancer and high-grade prostate cancer at biopsy. To our knowledge, this study is the first to investigate PPF quantity in patients without prior prostate cancer diagnosis.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".