Spatial distribution of positive cores improves the selection of patients with low‐risk prostate cancer as candidates for active surveillance
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that spatial distribution of positive cores at biopsy is a predictor of unfavourable prostate cancer characteristics at radical prostatectomy (RP) in active surveillance (AS) candidates. PATIENTS AND METHODS: We examined the data of 524 patients treated with RP, between 2000 and 2012. All fulfilled at least one of four commonly used AS criteria. Regression models tested the relationship between positive cores spatial distribution, defined as the number of positive zones at biopsy (PBxZ) and tumour laterality at biopsy and two endpoints: (i) unfavourable prostate cancer at RP (Gleason score ≥ 4 + 3, and/or pT3 disease), and (ii) clinically significant prostate cancer (tumour volume ≥ 2.5 mL). RESULTS: Unfavourable prostate cancer and clinically significant prostate cancer rates were 8 and 25%, respectively. Patients with more than one PBxZ had a 3.2-fold higher risk of harbouring unfavourable prostate cancer, and a 2.3-fold higher risk of harbouring clinically significant prostate cancer compared with their counterparts with one PBxZ (both P = 0.01). Patients with bilateral tumour at biopsy had a 3.3-fold higher risk of harbouring unfavourable prostate cancer and a 1.7-fold higher risk of harbouring clinically significant prostate cancer compared with their counterparts with unilateral tumour at biopsy (both P ≤ 0.04). Some of these results did not reach a statistically significant level, when the analyses were restricted to patients that fulfilled the most stringent AS criteria. CONCLUSIONS: Positive cores spatial distribution at biopsy should be considered, when advising patients about AS. The addition of this predictor to AS inclusion criteria can help identifying patients at a higher risk of progression, and reduce the rate of inappropriate surveillance of aggressive tumours. However, the most stringent AS criteria (namely John-Hopkins criteria and Prostate Cancer Research International: Active Surveillance criteria) might not benefit from the addition of this predictor. This point warrants further investigation in future studies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".