Histology results of systematic prostate biopsies by in-bore magnetic resonance imaging vs. transrectal ultrasound
Bibliographic record
Abstract
INTRODUCTION: We aimed to compare systematic biopsies (SBs) of in-bore magnetic resonance-guided prostate biopsy (MRGpB) with those performed under transrectal ultrasound (TRUS) guidance in the clinical setting. METHODS: Data on all 161 consecutive patients undergoing prostate biopsy at our institution between November 2017 and July 2019 were retrospectively collected. The patients were referred to biopsy due to elevated prostate-specific antigen (PSA) and/or abnormal digital rectal examination (DRE) and/or at least one Prostate Imaging Reporting and Data System (PI-RADS) lesion score of ≥3 on multiparametric magnetic resonance imaging (mpMRI). We included patients with PSA levels ≤20 ng/ml and those with 8-12 core biopsies. Histology results of SBs performed by in-bore MRGpB were compared to TRUS SBs. Chi-squared, Fischer's exact, and multivariate Pearson regression tests were used for statistical analysis (SPSS, IBM Corporation). RESULTS: In total, 128 patients were eligible for analysis. Their median age was 68 years (interquartile range [IQR] 61.5-72), mean prostate size 55±29 cc, and mean PSA and PSA density levels 7.6±3.5 ng/ml and 0.18±0.13 ng/ml/cc, respectively. Thirty-five patients (27.3%) had suspicious DRE findings. Both biopsy groups were similar for these parameters. Thirty-eight (62.3%) MRGpB patients had a previous biopsy vs. five (7.1%) TRUS-SB patients (p<0.0001). The number of patients diagnosed with clinically significant and non-significant disease was similar for both groups. High-risk disease was more prevalent in the TRUS-SB group (22.4% vs. 4.9%, p<0.01). CONCLUSIONS: Our data suggest that in-bore MRGpB is no better than TRUS for guiding SBs for the detection of clinically significant prostate cancer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".