MRI for Prostate Cancer Local Staging: <i>AJR</i> Expert Panel Narrative Review
Bibliographic record
Abstract
The incremental value of multiparametric MRI (mpMRI) in prostate cancer staging has been increasingly recognized, with the accumulated literature indicating a central role of MRI findings not only in detecting cancer but also in guiding local and systemic management. However, despite growing adoption of mpMRI for prostate cancer staging, no universally accepted framework for MRI-based staging exists. Additionally, proposed scoring systems for extraprostatic extension (EPE) remain outside of the PI-RADS structure, and reproducibility of MRI-based staging across centers is variable. This AJR Expert Panel Narrative Review synthesizes current evidence on the use of mpMRI for prostate cancer local staging, highlighting key MRI features that help distinguish organ-confined disease from tumors associated with EPE or seminal vesicle invasion, while considering such findings' implications for surgery and radiotherapy planning. Proposed structured frameworks for EPE reporting, based on MRI findings alone or MRI in combination with clinical parameters, are explored. Ongoing challenges and unmet needs are emphasized, including lack of reporting standardization, variable integration of mpMRI into international staging guidelines, and the need for prospective studies to validate the role of mpMRI staging in multidisciplinary prostate cancer care. Finally, potential future impacts of radiomics, artificial intelligence, and molecular imaging are considered.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".