Impact of Multiparametric Endorectal Coil Prostate Magnetic Resonance Imaging on Disease Reclassification Among Active Surveillance Candidates: A Prospective Cohort Study
Bibliographic record
Abstract
PURPOSE: We report magnetic resonance imaging findings among unselected men with low risk prostate cancer before active surveillance. MATERIALS AND METHODS: We prospectively enrolled men with low grade, low risk, localized prostate cancer. All patients underwent multiparametric endorectal coil magnetic resonance imaging and were offered confirmatory biopsy within 1 year of imaging. The primary outcome was the impact of magnetic resonance imaging on identifying patients who were reclassified by confirmatory biopsy as no longer fulfilling active surveillance criteria. We further identified clinical parameters associated with reclassification. The cohort was stratified as patients with 1) normal magnetic resonance imaging, 2) cancer on magnetic resonance imaging concordant with initial biopsy (less than 1 cm) and 3) cancer on magnetic resonance imaging larger than 1 cm. We performed univariate analysis to assess differences in clinical parameters among the groups. RESULTS: Magnetic resonance imaging did not detect cancer in 23 cases (38%) while magnetic resonance imaging and initial biopsy were concordant in 24 (40%). Magnetic resonance imaging detected a 1 cm or larger lesion in 13 patients (22%). Of the cases 18 (32.14%) were reclassified. When no cancer was identified on magnetic resonance imaging, only 2 cases (3.5%) were reclassified. The positive and negative predictive values for magnetic resonance imaging predicting reclassification were 83% (95% CI 73-93) and 81% (95% CI 71-91), respectively. Prostate specific antigen density was increased in patients with lesions larger than 1 cm on magnetic resonance imaging compared to those with no cancer on imaging (median 0.15 vs 0.07 ng/ml/cc, p=0.016). CONCLUSIONS: Magnetic resonance imaging appears to have a high yield for predicting reclassification among men who elect active surveillance. Upon confirmation of our results magnetic resonance imaging may be used to better select and guide patients before active surveillance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".