Magnetic resonance imaging detected prostate evasive anterior tumours: Further insights
Bibliographic record
Abstract
INTRODUCTION: Clinical confusion continues to exist regarding the underestimation of cancers among patients on active surveillance and among men with repeated negative prostate biopsies despite worrisome prostate-specific antigen (PSA) levels. We have previously described our initial experience with magnetic resonance imaging (MRI)-based detection of tumours in the anterior prostate gland. In this report, we update and expand our experience with these tumours in terms of multiparametric-MRI findings, staging, and grading. Furthermore, we report early treatment outcomes with these unique cancers. METHODS: We reviewed our prostate MRI dataset of 1117 cases from January 2006 until December 2012 and identified 189 patients who fulfilled criteria for prostate evasive anterior tumors (PEATS). Descriptive analyses were performed on multiple covariates. Kaplan-Meier actuarial technique was used to plot the treatment-related outcomes from PEATS tumours. RESULTS: Among the 189 patients who had MRI-detectable anterior tumours, 148 had biopsy proven disease in the anterior zone. Among these tumours, the average PSA was 18.3 ng/mL and most cancers were Gleason 7. In total, 68 patients chose surgical therapy. Among these men, most of their cancers had extra prostatic extension and 46% had positive surgical margins. Interestingly, upgrading of tumours that were biopsy Gleason 6 in the anterior zone was common, with 59% exhibiting upgrading to Gleason 7 or higher. Biochemical-free survival among men who elected surgery was not ideal, with 20% failing by 20 months. CONCLUSION: PEATS tumours are found late and are disproportionally high grade tumours. Careful consideration to MRI testing should be given to men at risk for PEATS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".