PURPOSES: Using population kinetic approach, we modeled PSA decline equations in patients with prostate cancer after radical prostatectomy (RP). We looked for relationships between early PSA decrease profile, characterized by PSA clearance (CL(PSA)) or half-life (HL(PSA)), and the 2-year biochemical relapse free survival (bRFS). PATIENTS AND METHODS: We performed a retrospective study on 55 patients treated with RP and with at least 2 PSA measurements in the post-operative month. A population kinetic model was investigated with NONMEM. The prognostic factors regarding bRFS were assessed using univariate and multivariate analyses. RESULTS: The best model describing the PSA post-operative decrease was bi-compartmental and fit patient data well. Median CL(PSA) was 0.034 (terciles were 0.023 and 0.048). The significant prognostic factors associated with a better bRFS with univariate analysis were lower CL(PSA) terciles (2-year bRFS = 100% vs. 85.1% vs. 66.7% if CL(PSA) < 0.023, 0.023 <or= CL(PSA) < 0.048 or CL(PSA) >or= 0.0480, P = 0.006) as well as initial PSA < 7 ng/ml, pT2 stage (vs. pT3), pN0 (vs. pN1) and low main Gleason score (3/5 vs. 4/5). Among these factors, CL(PSA) was the only independent prognostic factor with multivariate analysis regarding bRFS (HR = 0.92, 95%CI = [0.86-0.98], P = 0.0088). CONCLUSION: CL(PSA) determined with 4 PSA concentrations in the first month following the RP may predict the biochemical relapse risk of prostate cancer patients, thus enabling early identification of high-risk patients requiring adjuvant treatment. A prospective validation of these results is required.
PURPOSES: Using population kinetic approach, we modeled PSA decline equations in patients with prostate cancer after radical prostatectomy (RP). We looked for relationships between early PSA decrease profile, characterized by PSA clearance (CL(PSA)) or half-life (HL(PSA)), and the 2-year biochemical relapse free survival (bRFS). PATIENTS AND METHODS: We performed a retrospective study on 55 patients treated with RP and with at least 2 PSA measurements in the post-operative month. A population kinetic model was investigated with NONMEM. The prognostic factors regarding bRFS were assessed using univariate and multivariate analyses. RESULTS: The best model describing the PSA post-operative decrease was bi-compartmental and fit patient data well. Median CL(PSA) was 0.034 (terciles were 0.023 and 0.048). The significant prognostic factors associated with a better bRFS with univariate analysis were lower CL(PSA) terciles (2-year bRFS = 100% vs. 85.1% vs. 66.7% if CL(PSA) < 0.023, 0.023 <or= CL(PSA) < 0.048 or CL(PSA) >or= 0.0480, P = 0.006) as well as initial PSA < 7 ng/ml, pT2 stage (vs. pT3), pN0 (vs. pN1) and low main Gleason score (3/5 vs. 4/5). Among these factors, CL(PSA) was the only independent prognostic factor with multivariate analysis regarding bRFS (HR = 0.92, 95%CI = [0.86-0.98], P = 0.0088). CONCLUSION: CL(PSA) determined with 4 PSA concentrations in the first month following the RP may predict the biochemical relapse risk of prostate cancerpatients, thus enabling early identification of high-risk patients requiring adjuvant treatment. A prospective validation of these results is required.