Transrectal ultrasound-guided prostate biopsies vs. magnetic resonance imaging ultrasound fusion targeted biopsies: Who are the best candidates?
Bibliographic record
Abstract
Introduction: The aim of this study was to compare the results of ultrasound-guided prostate biopsies (US-PB) and magnetic resonance imaging-ultrasound fusion biopsies (MRI-PB) in two contemporary cohorts and to describe the parameters orienting the choice of technique.Methods: Two contemporary cohorts of patients undergoing US-PB or MR-PB using the Urostation® (Koelis, Grenoble, France) between November 2010 and July 2015 were analyzed retrospectively. Patients with metastatic cancer or recurrence after treatment, saturation biopsies, and US-PB performed after a negative MRI were excluded. Comparison of populations, biopsy results, and clinical and biological parameters guiding the choice of technique were studied on multivariate analysis (logistic regression) taking into account the following confounding factors: age, prostate-specific antigen (PSA) rate, prostatic volume, number of previous biopsies, and abnormal digital rectal examination.Results: One hundred fourteen patients were included in the US-PB group and 118 in the MR-PB group. Prostate cancer was diagnosed among 65 patients in the US-PB group (detection rate 57.0%) and 70 patients in the MR-PB group (detection rate 59.3%) (odds ratio [OR] 3.00; 95% confidence interval [CI] 1.52–6.17; p=0.002). Among the cancers diagnosed in the MR-PB group, 21 were diagnosed by the two targeted biopsy cores only (15.5%). Patients undergoing MR-PB were significantly younger (p=0.0005), with a higher number of previous biopsy sessions (p<10-7) and larger prostate volume (p=0.001). PSA rate alone (p=0.23) and digital rectal examination (p=0.48) did not significantly interfere with the choice of a technique.Conclusions: Younger patients with larger prostates and prior negative biopsy were more likely to be offered the MR-PB technique. On multivariate analysis, the detection rate was higher in the MR-PB group.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".