New Prostate MRI Scoring Systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB): <i>AJR</i> Expert Panel Narrative Review
Bibliographic record
Abstract
Multiparametric MRI (mpMRI), interpreted using PI-RADS, improves the initial detection of clinically significant prostate cancer. Prostate MR image quality has increasingly recognized relevance to the use of mpMRI for prostate cancer diagnosis. Additionally, mpMRI is increasingly used in scenarios beyond initial detection, including active surveillance and assessment for local recurrence after prostatectomy, radiation therapy, or focal therapy. In acknowledgment of these evolving demands, specialized prostate MRI scoring systems beyond PI-RADS have emerged to address distinct scenarios and unmet needs. Examples include Prostate Imaging Quality (PIQUAL) for assessment of image quality of mpMRI, Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations for evaluation of serial mpMRI examinations during active surveillance, Prostate Imaging for Recurrence Reporting (PI-RR) system for assessment for local recurrence after prostatectomy or radiation therapy, and Prostate Imaging after Focal Ablation (PI-FAB) for assessment for local recurrence after focal therapy. These systems' development and early uptake signal a compelling shift toward prostate MRI standardization in different scenarios, and ongoing research will help refine their roles in practice. This AJR Expert Panel Narrative Review critically examines these new prostate MRI scoring systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB), analyzing the available evidence, delineating current limitations, and proposing solutions for improvement.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".