Predicting disease progression in men with localized high risk prostate cancer undergoing radical prostatectomy.
Bibliographic record
Abstract
51 Background: Currently utilized pre-treatment nomograms for prostate cancer were developed and validated using populations primarily composed of men with low and intermediate risk disease. This study aims to construct a nomogram that predicts for biochemical recurrence (BCR) and metastasis (mets) from a contemporary cohort of men with with NCCN high (HR) and very high risk (VHR) prostate cancer. Methods: From 2005 to 2015, 1,241 men with NCCN HR or VHR prostate cancer were identified from two large academic medical centers. The cohort was divided into training (n = 620) and validation (n = 621) cohorts. Primary endpoints were BCR and mets. Cox multivariable regression was performed to model characteristics and outcomes in the training cohort. Model accuracy was assessed using the time-dependent area under the receiver operator characteristic curve (AUC) in the validation cohort. Results: 494 men (245 training and 249 validation) developed BCR, and 123 men (64 training and 59 validation) developed mets, with BCR-free and mets-free probability of 49.0% and 86.5% at 5- years, respectively. Predictive nomograms including age, ethnicity, PSA, Gleason grade, clinical stage, and the number of cores with Gleason 8-10 disease were developed. Models for BCR and mets had AUCs of 0.72 and 0.75. By comparison, the MSKCC preoperative nomogram and CAPRA nomogram provided AUCs of 0.69 and 0.68 for predicting BCR and 0.66 and 0.67 for mets. Conclusions: Individualized risk assessment is imperative for optimal decision making and to design and power clinical trials. The nomograms described here, created from a population exclusively comprised of HR/VHR men, have better discrimination than those previously established on cohorts of primarily low and intermediate risk men, and may represent an ideal way by which oncologic outcomes can be predicted in men with HR or VHR disease.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".