Impact of multiparametric endorectal coil prostate MRI on disease reclassification among active surveillance candidates: A prospective cohort study.
Bibliographic record
Abstract
30 Background: One troubling aspect of active surveillance (AS) is that a subset of patients diagnosed as very-low risk prostate cancer (PCa) appear to be under sampled and, in fact, harbour larger often higher grade cancers.The aim of this study is to report MRI findings among unselected men with low-risk PCa prior to AS. Methods: We prospectively enrolled men with low-grade, low-risk, localized PCa. All patients underwent multiparametric endorectal coil MRI scanning and offered a confirmatory biopsy within one year of MRI. The primary outcome was the impact of MRI in identifying patients reclassified as no longer fulfilling AS criteria by a confirmatory biopsy. We further aimed to identify clinical parameters associated with reclassification. Cohort was stratified as follows: normal MRI; cancer on MRI concordant with initial biopsy (less than 1 cm); cancer on MRI larger than 1cm. We performed a univariate analysis to assess differences in clinical parameters between groups. Results: MRI did not detect cancer in 23 (38%) while MRI and initial biopsy were concordant in 24 patients (40%). MRI detected a 1cm or larger lesion in 13 patients (22%). Eighteen patients (32.14%) reclassified. When no cancerous lesion was identified on MRI only 2 patients (3.5%) reclassified. The positive and negative predictive values for MRI predicating reclassification were 83% (95% CI, 73%-93%) and 81% (95% CI, 71%-91%), respectively. PSA density was elevated among patients with larger than 1 cm MRI lesions compared to those with no cancer on MRI (medians of 0.15 vs 0.07 ng/ml/cc, respectively p=0.016). Conclusions: MRI appears to have a high yield in predicting reclassification among men choosing AS. Upon confirmation of our results MRI may be used to better select and guide patients before AS.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".