Feng Qi1,2, Kai Zhu1, Yifei Cheng1, Lixin Hua1, Gong Cheng1. 1. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 2. Department of Urology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Abstract
OBJECTIVE: To develop a nomogram for selecting the "unreal" Gleason score (GS) 3 + 3 patients in biopsy GS 3 + 3 prostate cancer (PCa) patients. METHODS: Patients who were newly diagnosed with PCa by biopsy and underwent radical prostatectomy in the First Affiliated Hospital of Nanjing Medical University from January 2009 to October 2018 were enrolled. Comparisons were made between GS 3 + 3 and higher grade PCa patients. Logistic regression analysis was performed to determine the risk factors for the "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 patients. Then, a nomogram was developed to predict the probability of "unreal" GS 3 + 3 PCa according to the results of multivariate analysis. Finally, receiver operating characteristic and decision curve analysis (DCA) curves were structured to identify the efficiency of the predictive model. RESULTS: Compared to higher GS grade, biopsy GS 3 + 3 had greater upgrade risk (P < 0.05) while a lower proportion of positive surgical margins, seminal vesicle invasion, extra-prostatic extension, lymph node invasion, and nerve invasion (all P < 0.05). Multivariate analysis showed that age, PSAD, prostate imaging reporting and data system (PI-RADS) score and biopsy positive cores were significant risk factors for "unreal" GS 3 + 3. A nomogram was developed utilizing these factors with high prediction performance (area under curve = 0.924). Furthermore, DCA curve suggested that this predictive model was effective. CONCLUSIONS: The nomogram identified the probability of "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 PCa patients, which was of great value for clinical guidance in low risk PCa therapy.
OBJECTIVE: To develop a nomogram for selecting the "unreal" Gleason score (GS) 3 + 3 patients in biopsy GS 3 + 3 prostate cancer (PCa) patients. METHODS: Patients who were newly diagnosed with PCa by biopsy and underwent radical prostatectomy in the First Affiliated Hospital of Nanjing Medical University from January 2009 to October 2018 were enrolled. Comparisons were made between GS 3 + 3 and higher grade PCa patients. Logistic regression analysis was performed to determine the risk factors for the "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 patients. Then, a nomogram was developed to predict the probability of "unreal" GS 3 + 3 PCa according to the results of multivariate analysis. Finally, receiver operating characteristic and decision curve analysis (DCA) curves were structured to identify the efficiency of the predictive model. RESULTS: Compared to higher GS grade, biopsy GS 3 + 3 had greater upgrade risk (P < 0.05) while a lower proportion of positive surgical margins, seminal vesicle invasion, extra-prostatic extension, lymph node invasion, and nerve invasion (all P < 0.05). Multivariate analysis showed that age, PSAD, prostate imaging reporting and data system (PI-RADS) score and biopsy positive cores were significant risk factors for "unreal" GS 3 + 3. A nomogram was developed utilizing these factors with high prediction performance (area under curve = 0.924). Furthermore, DCA curve suggested that this predictive model was effective. CONCLUSIONS: The nomogram identified the probability of "unreal" GS 3 + 3 PCa in biopsy GS 3 + 3 PCa patients, which was of great value for clinical guidance in low risk PCa therapy.
Entities:
Keywords:
Gleason score; active surveillance; low risk; nomogram; prostate cancer; surgery