A prospective study of cancer detection rates following early repeat imaging and biopsy of PI-RADS 4 and 5 regions of interest exhibiting no clinically significant prostate cancer on prior biopsy
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine cancer detection rates following early repeat multiparametric magnetic resonance imaging (mpMRI) and biopsy of Prostate Imaging-Reporting and Data System (PI-RADS), v2.1 4 and 5 regions of interest (ROI) exhibiting no clinically significant prostate cancer (csPCa) on prior biopsy and to identify predictors for these missed csPCa. METHODS: Between January 2019 and August 2020, 36 men with 38 PI-RADS 4 or 5 ROI with no evidence of csPCa (defined as Gleason grade group [GGG] >1) on prior MRI fusion target biopsy (MRFTB) + systematic biopsy (SB) were invited to participate in the present prospective study. All men underwent repeat mpMRI and persistent PI-RADS >2 ROI were advised to undergo repeat MRFTB + SB. Cancer detection rates of any and csPCa were determined. Relative risk was calculated to analyze association of baseline variables with the finding of csPCa on repeat biopsy. RESULTS: Of the 38 initial PI-RADS 4 and 5 ROI, on followup mpMRI, 14 were downgraded to PI-RADS 1/2 and, per protocol, did not undergo repeat biopsy and; eight (33%), 12 (50%), and four (17%) were PI-RADS 3, 4, and 5, respectively. Of these 24 persistently suspicious mpMRI ROI, 20 (83%) underwent repeat biopsy and six (30%), six (30%), and eight (40%) were benign, GGG 1, and GGG >1, respectively. Only prostate-specific antigen ≥10 ng/mL was a predictor for missed csPCa. CONCLUSIONS: Our prospective study supports a recommendation for early repeat mpMRI of all PI-RADS 4 or 5 ROI exhibiting no csPCa, with repeat MRFTB + SB of persistent PI-RADS >2 ROI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".