The role of magnetic resonance imaging in targeting prostate cancer in patients with previous negative biopsies and elevated prostate‐specific antigen levels
Bibliographic record
Abstract
In the past 20 years, magnetic resonance imaging (MRI) has developed rapidly, along with the management of localized prostate cancer. We summarize current data on the efficacy of MRI for targeting cancer, compared with biopsies, in patients with previous negative prostate biopsies and persistently elevated prostate-specific antigen (PSA) levels. The key clinical question is how many men benefit by having had prostate cancer detected purely because of the MRI-targeted, as opposed to standard scheme, biopsies. We reviewed all available databases for prospective studies in patients having MRI and prostate biopsy with previous negative biopsies and persistently elevated PSA levels. Six studies fulfilled the selection criteria, with 215 patients in all; in these studies, the cancer-detection rate at repeat biopsy was 21-40%. For MRI or combined MRI/MR spectroscopy, the overall sensitivity for predicting positive biopsies was 57-100%, the specificity 44-96% and the accuracy 67-85%. In five studies, specific MRI-targeted biopsies and standard cores were taken, with a significant proportion (34/63, 54%) having cancer detected purely because of the MRI-targeted cores. The value of endorectal MRI and MR spectroscopy in patients with elevated PSA levels and previous negative biopsies to target peripheral zone tumours appears to be significant. Although more data obtained with current technologies are needed, published results to data are encouraging. A comparison study and cost-benefit analysis of MRI-targeted vs saturation biopsy in this group of patients would also be ideal, to delineate any advantages.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".