The role of quantitative MRI-based prostate zonal parameters in predicting clinically significant prostate cancer
Bibliographic record
Abstract
INTRODUCTION: We aimed to investigate the clinical utility of quantitative prostatic zonal measurements on multiparametric magnetic resonance imaging (mpMRI) for the predication of clinically significant prostate cancer (csPCa). METHODS: A retrospective, single-institution study included 144 men who underwent mpMRI from 2015-2017. Prostate zone parameters were measured on mpMRI. Correlation and multivariable analysis evaluated the relationship between prostate zone parameters and the presence of csPCa. RESULTS: The mean age was 66.9±7.8 years old. The median (interquartile range [IQR]) prostate volume and prostate-specific antigen (PSA) were 51.6 ml (37.1-74.5) and 6.1 ng/ ml (4.5-8.2), respectively. Men with csPCa had significantly smaller total prostate volume (TPV), transitional zone volume (TZV), and transitional zone thickness (TZT), and larger transitional zone density (TZD) compared to those without PCa; however, on multivariate variable analysis, only TZD maintained significance. TZD had a comparable area under the curve to PSA density (PSAD) and PSA (0.74 vs. 0.73 vs. 0.60, respectively). In a subgroup analysis of men with PCa, PSAD and TZD were significantly higher in men with Gleason grade group (GG) ≥2 compared to those with GG <2 (p=0.002); however, this significance is not maintained on logistic regression in predicting GG. CONCLUSIONS: Quantitative features of prostate zones on MRI may aid in identifying better predictors of csPCa. Zonal-based PSA density (TZD) may be a useful marker in identifying csPCa. Further exploration is needed to understand the clinical application of larger TZV in men with csPCa compared to those with insignificant 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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".