Pathological Concordance between Prostate Biopsies and Radical Prostatectomy Using Transperineal Sector Mapping Biopsies: Validation and Comparison with Transrectal Biopsies
Bibliographic record
Abstract
Background/Aims/Objectives: Our aim was to evaluate the accuracy of systematic transperineal sector mapping biopsy (TPSMB) in predicting Gleason score (GS) at radical prostatectomy (RP), to compare its accuracy with standard transrectal ultrasound-guided biopsies (TRUS) and to establish the clinical impact of discordance between biopsies and RP on subsequent surgical management. METHODS: Two hundred fifty-five patients from 2008 to 2013 who underwent RP following TPSMB (n = 204) or TRUS (n = 51), were included in this retrospective multi-institutional study. Concordance between biopsies and RPs GS was assessed both as percentages and with Cohen's Kappa coefficient. All mismatches between biopsies and RP were assessed for significance by 3 urologists using the Delphi method. RESULTS: No differences were present among the groups. Concordance between biopsy and RP GS was 75.49% for TPSMB and 64.70% for TRUS. Kappa coefficient was 0.42 and 0.39 respectively. The Delphi method showed lower clinical impact of GS discordances for TPSMB with 7.8% of patients having significant change, thus being potentially more suitable for other treatment modalities, compared to TRUS (13.7%). CONCLUSIONS: TPSMB had a higher accuracy for predicting the GS grade at RP showing superior GS concordance compared with standard TRUS. TPSMB provides an effective technique for systematic prostate biopsy to evaluate overall prostate cancer GS.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".