Prostatic zonal parameters and lower urinary tract symptoms as quantified via magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: Benign prostatic hyperplasia (BPH) is a common diagnosis among aging males; however, the relationship between prostate volume and lower urinary tract symptom (LUTS) severity is imperfect. The goal of this study was to comprehensively investigate the relationship between various prostate zone-based parameters measured using magnetic resonance imaging (MRI) and LUTS. METHODS: Data were retrospectively collected for 144 patients who underwent MRI between 2015 and 2017 at a single institution. Prostate volumes were measured on sagittal and axial T2 weighted using the prostate ellipsoid formula. RESULTS: Only transition zone thickness (TZT) correlated with International Prostate Symptom Score (IPSS) (Pearson's=0.33, p=0.007). The intraprostatic protrusion (IPP) component (rho=0.261, p=0.036), transitional zone volume (TZV) (rho=0.264, p=0.034), and TZT (Pearson's correlation=0.422, p<0.001) all correlated with worsening quality of life (QoL) scores. In total, 97.9% of men had the presence of an IPP (>0 mm) and larger IPPs were found in older men with higher postvoid residual volumes. Larger peripheral zone volume (PZV) (odds ratio [OR ] 3.62, 95% confidence interval [CI] 1.07-12.30, p<0.05), TZV (OR 6.00, 95% CI 1.69-21.35, p<0.05), and TZT (OR 4.00, 95% CI 1.17-13.69, p<0.05) were predictive of developing severe LUTS ; however, IPP (p=0.122) was not. CONCLUSIONS: TZV, TZT, and IPP all demonstrated a role in the evaluation of LUTS, with predictive capabilities. IPP is very common but not always clinically significant. Clarifying more precise zonal parameters and their relationship with LUTS may ultimately help clinicians guide the need for surgical intervention more precisely.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".