Body mass weighted prostate-specific antigen levels, new markers to predict locally advanced prostate cancer after prostatectomy.

Abstract

Background: Prostate-specific antigen (PSA) remains the most useful marker for screening, risk categorization, and follow-up in patients with prostate cancer. In the obese population, several studies have revealed that obesity may not only inversely interfere with the concentration of PSA, but also increase the risk of prostate cancer. Thus, we considered using the body mass weighted PSA levels, presented as serum PSA concentration multiplied by body weight or body mass index (BMI), instead of traditional PSA concentration, as potential markers to predict locally advanced prostate cancer after prostatectomy.

Methods: We retrospectively collected and analyzed data acquired from a single institute at which robot-assisted laparoscopic radical prostatectomy was performed. A total of 174 patients underwent radical prostatectomy, and the collected data included age, PSA level, body weight, BMI, and pathology results.

Results: A total of 174 patients were diagnosed with adenocarcinoma of the prostate by needle biopsy, and most (N = 165) were considered to have localized disease on preoperative multiparameter magnetic resonance imaging. After prostatectomy, 73% (N = 127) of the patients remained in the localized disease group (group A) and 27% (N = 47) of the patients were reclassified to the locally advanced prostate cancer (group B). The value of PSA was higher in group B (16.9 vs 11.2 ng/dL; p = 0.062), but there was no statistically significant difference between the two groups. After using the numerical values of PSA × body weight and PSA × BMI, a statistically significant difference emerged between the two groups ( p = 0.0198 in PSA × BW; p = 0.0110 in PSA × BMI).

Conclusion: The body mass-weighted PSA levels, instead of the traditional PSA concentration, may be better markers for predicting non-organ-confined disease after surgery. It may also be useful in screening and risk categorization.