When can active surveillance be less active? Prediction of long-term nonreclassification for men with low-risk prostate cancer.
Bibliographic record
Abstract
140 Background: Active surveillance is endorsed as the preferred management strategy for most men with low-risk prostate cancer. However, nearly all active surveillance protocols entail prostate specific antigen (PSA) testing every 3-6 months, and prostate biopsies every 1-2 years. For many men with indolent tumors, this regimen is overly intense, and exposes men to the discomfort, risks, and costs of repeated biopsies. We aimed to determine if some men can be safely selected for a less intense surveillance regimen by predicting the probability of non-reclassification over the next 4 years of surveillance. Methods: Data were collected from men in the multicenter Canary Prostate Active Surveillance Study (PASS), in which PSAs are collected q3 months and biopsies performed 12 months of diagnosis and then every 2 years. For inclusion in this study, men had to have undergone ≤ 1 follow up biopsy, and have Gleason grade group 1 at diagnosis. Reclassification was defined as increase in Gleason grade group on subsequent biopsy; those without reclassification were censored at last study contact, treatment or 2 years after last biopsy. A dynamic risk prediction model based on a Cox regression with robust variance estimates was used to construct and test a model predicting non-reclassification. Results: Of 1082 men included, 362 (33%) reclassified and the remaining were censored. The final regression model included percent of biopsy cores involved, prior biopsy history, time since diagnosis, BMI, prostate size, diagnostic PSA, and PSAk (a measure of PSA kinetics). This dynamic risk prediction model was assessed at a measurement time of 1 year after diagnosis, predicting risk of reclassification at 4 years. Men at lowest and highest deciles of this model-based risk faced 6% (95%CI 0-12%) and 73% (55-84%) risks of reclassification within 5 years. For at least 10% of the men in the cohort, the negative predictive value (NPV) for reclassification was 95% or higher. Conclusions: A substantial proportion of men with low-risk prostate cancer can safely be followed with a de-intensified active surveillance protocol, which would improve both the tolerability and cost-effectiveness of this management strategy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".