‘Prostatic evasive anterior tumours’: the role of magnetic resonance imaging
Bibliographic record
Abstract
OBJECTIVE: To review our experience and delineate the role of magnetic resonance imaging (MRI) in identifying patients presenting with a raised prostate-specific antigen (PSA) level and clinical findings suggestive of anterior predominant tumours, which appear to be significant, particularly in those with a previous negative biopsy or low-volume disease undergoing active surveillance. PATIENTS AND METHODS: We retrospectively reviewed our database to identify patients with anteriorly predominant tumours on MRI whom had undergone prostate biopsy. RESULTS: In all, 31 patients with anterior predominant tumours on MRI also had a positive biopsy (14 on active surveillance and 17 with previous negative biopsies). MRI was usually invoked by the presenting PSA level or PSA velocity. MRI had a positive predictive value for anterior tumours of 87% (27/31). The Gleason score distribution for the 27 men with cancer was 6 in 15; 3 + 4 in three, 4 + 3 in six and 8/9 in three. For prostatic cores, 44/85 (52%) samples from the anterior prostate had cancer. Thirteen patients had a radical prostatectomy (pT2 in three, pT3 in seven and pT4 in three); seven of the 13 had positive surgical margins and a third of them had a biochemical recurrence at the 1-year follow-up. CONCLUSION: There is a subset of patients either having a negative biopsy or low-volume disease and who are on active surveillance who should be considered for MRI and further biopsy, as their pathology might be aggressive. An entity might be emerging with anterior predominant tumours that are impalpable, and we believe the term 'prostate evasive anterior tumour syndrome' to be appropriate. This requires further analysis in a large prospective database with consideration for triggers for MRI and targeted biopsies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".