Proenzyme psa for the early detection of prostate cancer in the 2.5–4.0 ng/ml total psa range: preliminary analysis

Introduction: Total PSA (tPSA) and free PSA (fPSA) are the most commonly used biomarkers for early detection of prostate cancer. Despite standardization efforts, many available PSA assays may still produce discordant results. In the present study, we compared four PSA assays calibrated to the WHO standards 96/670 and 96/668 for tPSA and fPSA, respectively. Methods: Within the scope of the Prostate Cancer Early Detection Study Based on a ‘‘Baseline’’ PSA Value in Young Men (PROBASE), we tested tPSA and fPSA in serum samples from 50 patients in the four different PROBASE sites using four WHO-calibrated assays from Roche (Elecsys, Cobas), Beckman-Coulter (Access-II) and Siemens (ADVIA Centaur). The comparison was performed using the Passing–Bablok regression method. Results: Compared to Access, the median tPSA levels for Centaur, Elecsys, and Cobas were +3%, +11%–20%, and +17%–23%, respectively, while for median fPSA levels the differences for Centaur, Elecsys, and Cobas were +49%, +29%–31%, and +22%, respectively. Discussion: Despite all investigated assays being WHO-calibrated, the Elecsys and Cobas tPSA assays produced considerably higher results than the Access and Centaur assays. Differences in fPSA-recovery between all investigated assays were even more pronounced. When applying the tPSA cutoff of 3.1 μg/L recommended for WHO-calibrated assays, the use of higher calibrated assays may lead to unnecessary prostate biopsies. Conversely, if the historical threshold of 4 μg/L is applied when using WHO-calibrated assays, it could lead to falsely omitted prostate biopsies.

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Novel Artificial Neural Network for Early Detection of Prostate Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2002.20.4.921 ◽

2002 ◽

Vol 20 (4) ◽

pp. 921-929 ◽

Cited By ~ 55

Author(s):

Bob Djavan ◽

Mesut Remzi ◽

Alexandre Zlotta ◽

Christian Seitz ◽

Peter Snow ◽

...

Keyword(s):

Prostate Cancer ◽

Early Detection ◽

Roc Curve ◽

Predictive Accuracy ◽

Specific Antigen ◽

Psa Density ◽

Free Psa ◽

Ann Models ◽

Artificial Neural ◽

Total Psa

PURPOSE: Two artificial neural networks (ANN) for the early detection of prostate cancer in men with total prostate-specific antigen (PSA) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospectively developed. The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of standard PSA parameters. PATIENTS AND METHODS: Consecutive men with a serum total PSA level between 4 and 10 ng/mL (n = 974) and between 2.5 and 4 ng/mL (n = 272) were analyzed. A separate ANN model was developed for each group of patients. Analyses were performed to determine the presence of prostate cancer. RESULTS: The area under the receiver operator characteristic (ROC) curve (AUC) was 87.6% and 91.3% for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models, respectively. For the latter model, the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA, percentage of free PSA, PSA density of the transition zone (TZ), and TZ volume (P < .01), but not significantly higher compared with multivariate analysis. For the 2.5 to 4 ng/mL model, the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA (P = .0239), PSA-TZ (P = .0204), and PSA density and total prostate volume (P < .01 for both). CONCLUSION: The predictive accuracy of the ANN was superior to that of conventional PSA parameters. ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy.

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