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Measurement of peri‐prostatic fat thickness using transrectal ultrasonography (TRUS): a new risk factor for prostate cancer

2012· article· en· W1723191614 on OpenAlex
Bimal Bhindi, Greg Trottier, Malik Elharram, Kimberly A. Fernandes, Gina Lockwood, Ants Toi, Karen Hersey, Antonio Finelli, Andrew Evans, Theodorus van der Kwast, Neil Fleshner

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsCanadian Partnership Against CancerUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineProstate cancerTransrectal ultrasonographyProstateRisk factorCancerProstate biopsyUrologyPopulationOncologyBiopsyInternal medicineGynecology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.270
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it