01 February 2011
PSA mass as a marker of prostate cancer progression after radical prostatectomy
Piotr BryniarskiABCDEF, Andrzej ParadyszAD, Mieczyslaw FryczkowskiABDDOI: 10.12659/MSM.881395
Med Sci Monit 2011; 17(2): CR104-109
Abstract
Background: Obese patients with prostate cancer may have lower preoperative PSA concentration due to hemodilution. Lower PSA concentration may falsely affect assessing the risk of progression after radical prostatectomy (RP). The aim of this study was to determine preoperative PSA mass as the absolute amount of PSA protein secreted into circulation, and evaluation of its usefulness in prediction of biochemical recurrence after RP.
Material/Methods: 177 patients after RP due to prostate cancer were included in the study. On the basis of formulas, PSA mass was calculated {PSA mass [µg] = (weight [kg])0.425 × (height [cm])0.72 × 0.007184 × 1.670 × PSA concentration [ng/ml]}. Patients were divided into 3 groups according to increasing values of PSA mass. The following features were assessed and compared between these groups (χ-square test): pathologic stage T3, nodal metastases, positive surgical margins, biochemical and local recurrence and the rate of death. Cancer-specific survival was assessed depending on PSA mass (Kaplan-Meier curves with log rank test). The usefulness of PSA mass in prediction of biochemical recurrence was compared with PSA concentration (logistic regression with ROC curves).
Results: Pathologic stage T3, nodal metastases, positive surgical margins and progression were more common in patients with higher levels of PSA mass (p<0.01). Cancer-specific survival was significantly shorter in patients with elevated values of PSA mass (p=0.02). Preoperative PSA mass was a more sensitive predictor of biochemical recurrence than was PSA concentration (p=0.04).
Conclusions: The preoperative PSA mass is a better predictor of biochemical recurrence after RP than PSA concentration.
Keywords: Prostate-Specific Antigen - chemistry, Prostatectomy, Predictive Value of Tests, Odds Ratio, Molecular Weight, Logistic Models, Disease Progression, Body Mass Index, Prostatic Neoplasms - surgery, ROC Curve, Recurrence, Tumor Markers, Biological - metabolism
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