The Role of Tumor Volume Ratio in Predicting Clinically Significant Prostate Cancer on Transperineal Biopsy
Bibliographic record
Abstract
Objectives:Multiparametric magnetic resonance imaging (mpMRI) has made dramatic inroads into the management of localized prostate cancer (PCa); however, not all suspicious lesions represent clinically significant (cs) PCa. We aimed to analyze the hypothetical effect of incorporating tumor volume ratio (TVR) into prostate biopsy (PBx) decision-making. Materials and Methods:Two hundred and fifty-two patients with suspicious lesions at mpMRI undergoing transperineal PBx under local anesthesia between 2019 and 2022 were retrospectively evaluated. TVR was calculated by dividing the tumor volume by the prostate volume. A regression model was used to assess predictors of csPCa. Descriptive statistics were applied to evaluate the effect of including TVR in PBx decision-making. Results:Overall, 119 patients (47%) were found to have csPCa. Age (p < 0.001), prior negative PBx (p = 0.011), and TVR (p < 0.001) were found to be independent predictors of csPCa. Applying the TVR cutoff of 0.23, a total of 117/252 (46%) PBx would have been avoided at the cost of missing csPCa in 26 (10%) men. Conclusions:Age, previous biopsy status, and TVR were found to be independent predictors of csPCa in men with suspicious lesions at mpMRI. Implementation of TVR into PBx decision-making improves the accuracy of mpMRI. Future studies are required to validate our findings and evaluate the role of TVR in avoiding unnecessary PBx.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".