Success of targeted transperineal biopsy in patients on surveillance for grade group 1 prostate cancer
Bibliographic record
Abstract
Introduction: We aimed to determine the minimum cross-sectional ellipsoid area on magnetic resonance (MR) of intraprostatic nodules that best predicts for subsequent targeted biopsies revealing ≥ grade group (GG) 2 disease. Methods: Forty-six patients previously diagnosed with GG 1 prostate adenocarcinoma who received cognitively fused, MR-guided, transperineal targeted biopsies in addition to six random biopsies were included in this analysis. A Youden cutpoint analysis was used to determine the ellipsoid area in the axial plane best predicting for ≥GG 2 disease within the targeted biopsy cores and logistic regression used to assess the result. Results: Median time from MR imaging to targeted biopsy was 2.4 (1.4–5.5) months. Forty of 46 (87%) patients had one nodule and 6/46 (13%) had two separate nodules on MR that received targeted biopsy. Of the 52 nodules, five (10%), 33 (63%), and 14 (27%) were Prostate Imaging–Reporting and Data System (PI-RADS) 3, 4, and 5. Thirteen (25%), six (12%), and 33 (64%) were in the anterior, medial, and posterior regions of the prostate. Median area was 0.72 (0.49–1.29) cm2 (average diameter 9.5 mm). Fifteen of 46 (33%) patients had ≥1 random biopsy and 20/52 (38%) nodules had ≥1 targeted biopsy revealing ≥GG 2 disease. The optimal area cutpoint was ≥0.7cm2, with an area under the curve of 0.671 (0.510–0.832). On logistic regression, areas ≥0.7 cm2 was solely predictive of targeted biopsy revealing ≥GG 2 disease (odds ratio 6.5, 1.3–32.4, p=0.022). Conclusions: Nodule area ≥0.7 cm2 may predict for transperineal-based targeted biopsies being positive for ≥GG 2 disease when 1–2 cores are taken.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".