Value of Increasing Biopsy Cores per Target with Cognitive MRI-targeted Transrectal US Prostate Biopsy
Bibliographic record
Abstract
Purpose To determine the increase in clinically significant cancer detection in the prostate with increasing number of core samples obtained by using cognitive MRI-targeted transrectal US biopsy. Materials and Methods This retrospective cross-sectional study included 330 consecutive patients (mean age, 64.3 years; range, 42-84 years) who underwent multiparametric prostate MRI from March 2012 to July 2017 and had an index lesion that subsequently underwent cognitive MRI-targeted biopsy using transrectal US with at least five core samples (which were sequentially labeled) per lesion. The detection rate of clinically significant cancer was calculated on sequential biopsy cores, comparing the first core alone versus three cores versus five cores per target. Clinically significant cancer was defined as International Society of Urological Pathology Grade Group 2 or higher. Results Increasing the number of biopsy core samples from one to three per target and from three to five per target increased the detection rate of clinically significant cancer by 6.4% (21 of 330) and 2.4% (eight of 330), respectively. The target yield for clinically significant cancer was 26% (87 of 330), 33% (108 of 330), and 35% (116 of 330) for one, three, and five cores, respectively. Subgroup analysis showed no significant difference in upgrade rates as a function of multiparametric MRI lesion size (P = .53-.59) or location (P = .28-.89). Conclusion More clinically significant prostate cancers are detected when increasing the number of core biopsy samples per index lesion from one to three and from three to five (6.4% and 2.4%, respectively) when performing cognitive MRI-targeted transrectal US biopsy. © RSNA, 2019 See also the editorial by Oto in this issue.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".