Objective. To assess the effectiveness of gonadotropin-releasing hormone (GnRH) antagonists and agonists in the treatment of patients with hormone-sensitive prostate cancer (HSPC), thus providing valid data support for their clinical treatment. Methods. We collected 52 and 65 HSPC patients treated with GnRH antagonists and agonists, respectively, in Tongji Hospital, Tongji Medical College of HUST between May 2019 and April 2021. Prostate-specific antigen (PSA) levels before and after treatment were recorded and analyzed. Further, univariate and multivariate logistic regressions were used to analyze the influencing factors of PSA control rate in HSPC patients. Results. In patients receiving antagonist, the control rate of prostate-specific antigen (PSA) was 54.28% and 88% without and with abiraterone, respectively, and 47.91% and 72% in patients treated using agonist without and with abiraterone. In 32 pairs of patients obtained via propensity score matching, the PSA control rates were 84.38% and 53.13% for those receiving antagonists and agonists, respectively, and 66.67% and 50% for those without abiraterone, respectively. In addition, univariate logistic regression analysis showed that the type of androgen deprivation therapy (ADT) drugs and combined use of abiraterone had a significant effect on the control rate of PSA. Further multivariate logistic regression revealed that GnRH antagonists in ADT drugs were risk factors for PSA control rate. Conclusion. The PSA control rate of HSPC patients treated with GnRH antagonist is significantly higher than that of the agonist group, and the use of GnRH antagonist is an independent predictor of PSA control rate.

Background Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. Methods We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. Results 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573–0.853), specificity of 0.869 (95% CI 0.764–0.974), F1 of 0.699 (95% CI 0.557–0.841), and AUC of 0.864 (95% CI 0.794–0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. Conclusion Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.