MRI in Prostate Cancer Screening: A Review and Recommendations, From the <i>AJR</i> Special Series on Screening
Bibliographic record
Abstract
Traditional PSA-based screening for prostate cancer (PCa) is challenged by an unfavorable benefit-to-harm ratio from underdiagnosis of clinically significant cancers, overdiagnosis of indolent cancers, and unnecessary biopsies, despite demonstrated reductions in PCa-associated mortality. Inclusion of MRI in screening algorithms helps address these limitations by improving risk stratification of men suspected of having PCa and by enabling targeted biopsies. The impact of MRI-based strategies on screening's benefit-to-harm ratio can be objectively assessed using ratios reflecting clinically significant cancers detected, indolent cancers detected, unproductive biopsies, and avoided biopsies. Of two overarching MRI-based screening strategies (sequential MRI after PSA testing and MRI alone), the sequential strategy is favored as a balanced and scalable approach. This Special Series Review provides a detailed analysis of the role of MRI in PCa screening, targeted to radiologists. Recommendations are provided for optimizing the use of MRI in PCa screening, including individualized risk assessments, tailored protocols, quality assurance for ensuring reliable and reproducible results, and consideration of new screening-specific scoring systems and biopsy thresholds. Ultimately, successful integration of MRI in PCa screening will require radiologists to actively engage in refining protocols, standardizing interpretations, and adopting emerging technologies. Such efforts will help maximize benefits while minimizing harms, enabling wider acceptance of PCa screening.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".