Clinical assessment of urinary prostate cancer antigen 3 in Chinese population: a large-scale, prospective and multicenter study
Bibliographic record
Abstract
BACKGROUND: To assess the clinical utility of PCA3 in the diagnostic accuracy, the correlation between PCA3 and biopsy or pathological characteristics and the performance of PCA3 to reduce the unnecessary biopsies in Chinese population. METHODS: A prospective study including patients with indication of prostate biopsies from 4 centers was conducted. All patients underwent PCA3 urine tests and prostate biopsies. The PCA3 score was analyzed by PCA3 gene expression Detection Kit (Fluorescent RT-PCR) (York biotech, Cat.#YDM-B01, China). Base model (clinical information) and PCA3 model (PCA3 scores and clinical information) were constructed via multivariate logistic regression. Discrimination, calibration and decision curve analysis were evaluated. RESULTS: In 1117 patients, 587 men with positive biopsy results had higher median PCA3 scores than those with negative biopsy results (p < 0.001). PCA3 scores had a greater area under the curve (AUC) than tPSA, %fPSA and PSAD in all PSA levels or PSA gray zone (4-10 ng/ml). Men with biopsy Gleason score < 7 had lower median PCA3 scores than those with Gleason score ≥ 7 (p = 0.016). In radical prostatectomy specimens, PCA3 scores were significantly associated with high-grade PCa (p = 0.002) and EAU biochemical recurrence risk (p = 0.044), but not extracapsular extension (p = 0.072), seminal vesicle invasion (p = 0.482) and T stage (p = 0.457). Regression analysis showed that the AUC increased from 0.806 (base model) to 0.873 (PCA3 model). PCA3 model with cutoff 0.15 could reduce 35.3% prostate biopsies and delay 5.8% high-grade PCa. CONCLUSIONS: PCA3 had a better diagnosis accuracy than tPSA, %fPSA and PSAD. PCA3 was a significantly independent predictor for risk stratification, suggesting that PCA3 could provide incremental value to reduce unnecessary prostate biopsies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".