PI-RR: The Prostate Imaging for Recurrence Reporting System for MRI Assessment of Local Prostate Cancer Recurrence After Radiation Therapy or Radical Prostatectomy—A Review
Bibliographic record
Abstract
The purpose of this article is to review clinical application of the Prostate Imaging for Recurrence Reporting (PI-RR) system. This system, released in 2021, represents international consensus-based guidelines for the acquisition, interpretation, and reporting of multiparametric MRI performed to detect locally recurrent prostate cancer after radiation therapy or radical prostatectomy. The system reduces variability through use of a standardized and structured reporting approach whereby the overall level of suspicion of recurrence is classified on a 5-point scale. The overall suspicion score is derived from 5-point scales for assessing DWI and dynamic contrast-enhanced (DCE) imaging. Separate scales for both DWI and DCE imaging are provided for evaluation after radiation therapy and after radical prostatectomy. These scales account for the relation between detected abnormalities and the location of the primary tumor on pretreatment imaging. T2-weighted imaging is also assessed on a 5-point scale and is useful for anatomic imaging but does not influence the overall score. Initial retrospective studies have shown promising results with respect to the reproducibility and accuracy of PI-RR in detecting locally recurrent tumor.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".