Multiparametric magnetic resonance imaging: Current role in prostate cancer management
Bibliographic record
Abstract
Digital rectal examination, serum prostate-specific antigen screening and transrectal ultrasound-guided biopsy are conventionally used as screening, diagnostic and surveillance tools for prostate cancer. However, they have limited sensitivity and specificity. In recent years, the role of multiparametric magnetic resonance imaging has steadily grown, and is now part of the standard clinical management in many institutions. In multiparametric magnetic resonance imaging, the morphological assessment of T2-weighted imaging is correlated with diffusion-weighted imaging, dynamic contrast-enhanced imaging perfusion and/or magnetic resonance spectroscopic imaging. Multiparametric magnetic resonance imaging is currently regarded as the most sensitive and specific imaging technique for the evaluation of prostate cancer, including detection, staging, localization and aggressiveness evaluation. This article presents an overview of multiparametric magnetic resonance imaging, and discusses the current role of multiparametric magnetic resonance imaging in the different fields of prostate cancer management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".