Transition from Transrectal to Transperineal MRI-Fusion Prostate Biopsy Does Not Comprise Detection Rates of Clinically Significant Prostate Cancer at a Tertiary Care Center
Bibliographic record
Abstract
Background: A remarkable paradigm shift has emerged regarding the preferred prostate biopsy approach, favoring the transperineal (TP) over the transrectal (TR) approach due to the reduced risk of severe urinary tract infections. However, its impact on the detection of clinically significant prostate cancer (csPCa) remains unclear. Materials and methods: We relied on a prospectively maintained tertiary care database to identify patients who underwent either TP or TR prostate biopsy between 01/2014 and 12/2023. Of those, only patients with suspicious magnetic resonance imaging (MRI) PIRADS lesions (Likert-scale: 3,4,5) received MRI-targeted and systematic biopsies. Detection rates of csPCa (International Society of Urological Pathology [ISUP] ≥ 2) were compared between biopsy approach (TP vs. TR) according to index lesion. Subsequently, uni- and multivariable logistic regression models were applied to investigate the predictive status of the biopsy approach within each subcohort. Results: Of 2063 patients, 1118 (54%) underwent combined MRI-guided and systematic prostate biopsy and were included in the final cohort. Of those, 127 (11%) and 991 (89%) underwent TP vs. TR. CsPCa rates, regardless of differences in patients’ demographics and distribution of index PIRDAS lesions, did not differ statistically significantly and were 51 vs. 52%, respectively (p = 0.8). CsPCa detection rates for PIRDAS-3, PIRADS-4 and PIRADS-5 did not differ and were 24 vs. 23%, 48 vs. 51% and 72 vs. 76% for PIRADS-3, PIRADS-4 and PIRADS-5 subgroups for TP vs. TR, respectively (all p ≥ 0.9) Conclusions: The current results support the available data indicating that TP biopsy approach is comparable to transrectal biopsy approach regarding csPCa detection rates.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".