PCA3: A Molecular Urine Assay for Predicting Prostate Biopsy Outcome
Bibliographic record
Abstract
PURPOSE: A urinary assay for PCA3, an mRNA that is highly over expressed in prostate cancer cells, has shown usefulness as a diagnostic test for this common malignancy. We further characterized PCA3 performance in different groups of men and determined whether the PCA3 score could synergize with other clinical information to predict biopsy outcome. MATERIALS AND METHODS: Prospectively urine was collected following standardized digital rectal examination in 570 men immediately before prostate biopsy. Urinary PCA3 mRNA levels were quantified and then normalized to the amount of prostate derived RNA to generate a PCA3 score. RESULTS: The percent of biopsy positive men identified increased directly with the PCA3 score. PCA3 assay performance was equivalent in the first vs previous negative biopsy groups with an area under the ROC curve of 0.70 and 0.68, respectively. Unlike serum prostate specific antigen the PCA3 score did not increase with prostate volume. PCA3 assay sensitivity and specificity were equivalent at serum prostate specific antigen less than 4, 4 to 10 and more than 10 ng/ml. A logistic regression algorithm using PCA3, serum prostate specific antigen, prostate volume and digital rectal examination result increased the AUC from 0.69 for PCA3 alone to 0.75 (p = 0.0002). CONCLUSIONS: PCA3 is independent of prostate volume, serum prostate specific antigen level and the number of prior biopsies. The quantitative PCA3 score correlated with the probability of positive biopsy. Logistic regression results suggest that the PCA3 score could be incorporated into a nomogram for improved prediction of biopsy outcome. The results of this study provide further evidence that PCA3 is a useful adjunct to current methods for prostate cancer diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".