The Significance of Prostate Specific Antigen Persistence in Prostate Cancer Risk Groups on Long-Term Oncological Outcomes

Long-Term Prognosis and Treatment Strategy of Persistent PSA After Radical Prostatectomy

PURPOSE

Prostate-specific antigen (PSA) is thought to be undetectable (< 0.1 ng/mL) after radical prostatectomy (RP), and persistent PSA (≥ 0.1 ng/mL) is considered a failure of curative treatment.

MATERIALS AND METHODS

The study population consisted of 135 patients, all of whom underwent RP for localized prostate cancer, and developed persistent PSA. We set the starting point at the timing of RP, and the endpoints were the development of castration-resistant prostate cancer (CRPC) and cancer-specific survival.

RESULTS

Salvage radiation therapy (RT) and androgen deprivation therapy (ADT) were performed in 53 (39.3%) and 64 (47.4%) patients, respectively. Eighteen (13.3%) patients didn't receive any salvage treatment. During the median follow-up of 10.1 years, CRPC was observed in 23 patients, and 6 patients died due to prostate cancer. Kaplan-Meier curves demonstrated the 15-year CRPC-free and cancer-specific survivals were 79.5% and 92.7%, respectively. Cox multivariate analysis demonstrated that seminal vesicle invasion (SVI) (p = 0.007) and nadir PSA ≥1.0 ng/mL (p = 0.002) were independent risk factors for CRPC. Salvage RT demonstrated better cancer control (the 10-and 15-year CRPC-free survival was 94.1% and 94.1%) compared to ADT (75.9% and 58.5%, p = 0.017) after 1:1 propensity score matching.

CONCLUSIONS

SVI and nadir PSA ≥1.0 ng/mL are independent risk factors for CRPC in patients with persistent PSA after RP. Salvage RT is considered to be the optimal treatment for this condition.

Clinicopathological and oncological significance of persistent prostate-specific antigen after radical prostatectomy: A systematic review and meta-analysis

Objective

To investigate the association of persistently elevated prostate-specific antigen (PSA) after radical prostatectomy (RP) with clinicopathological features and long-term oncological prognosis for the development of a potential management strategy.

Methods

A systematic literature search was performed using PubMed and Web of Science up to June 2021 to identify the eligible studies focusing on understanding the impact of persistent PSA in patients who underwent RP for localized prostate cancer. Meta-analyses were performed on parameters with available information.

Results

A total of 32 RP studies were identified, of which 11 included 26 719 patients with consecutive cohorts and the remaining 21 comprised 24 177 patients with cohorts carrying specific restrictions. Of the 11 studies with consecutive cohorts, the incidence of persistent PSA varied between 3.1% and 34.6% with a median of 11.0%. Meta-analyses revealed patients with persistent PSA consistently showed unfavorable clinicopathological features and a more than 3.5-fold risk of poorer biochemical recurrence, metastasis, and prostate cancer-specific mortality prognosis independently, when compared to patients with undetectable PSA. Similarly, cases with persistent PSA in different specific patient cohorts with a higher risk of prostate cancer also showed a trend of worse outcomes.

Conclusion

We found that the frequency of persistent PSA was about 11.0% in consecutive RP cohorts. Persistent PSA was significantly associated with unfavorable clinicopathological characteristics and worse oncological outcomes. Patients with persistent PSA after RP may benefit from early salvage treatment to delay or prevent biochemical recurrence, improving oncological outcomes for these patients. Further prospective randomized controlled trials are warranted to understand optimal systemic therapy in these patients.

A New Goodness of Fit Test for Multivariate Normality and Comparative Simulation Study

The testing of multivariate normality remains a significant scientific problem. Although it is being extensively researched, it is still unclear how to choose the best test based on the sample size, variance, covariance matrix and others. In order to contribute to this field, a new goodness of fit test for multivariate normality is introduced. This test is based on the mean absolute deviation of the empirical distribution density from the theoretical distribution density. A new test was compared with the most popular tests in terms of empirical power. The power of the tests was estimated for the selected alternative distributions and examined by the Monte Carlo modeling method for the chosen sample sizes and dimensions. Based on the modeling results, it can be concluded that a new test is one of the most powerful tests for checking multivariate normality, especially for smaller samples. In addition, the assumption of normality of two real data sets was checked.