Xiaochuan Wang1, Yu Zhang1, Fengbo Zhang1, Zhengguo Ji1, Peiqian Yang1, Ye Tian2. 1. Department of Urology, Capital Medical University Affiliated Beijing Friendship Hospital, No. 95, Yongan Road, Xicheng District, Beijing, People's Republic of China. 2. Department of Urology, Capital Medical University Affiliated Beijing Friendship Hospital, No. 95, Yongan Road, Xicheng District, Beijing, People's Republic of China. tianye166@126.com.
Abstract
BACKGROUND: To explore the rate of Gleason sum upgrading (GSU) from biopsy to radical prostatectomy pathology and to develop a nomogram for predicting the probability of GSU in a Chinese cohort. METHODS: We retrospectively reviewed our prospectively maintained prostate cancer (PCa) database from October 2012 to April 2020. 198 patients who met the criteria were enrolled. Multivariable logistic regression analysis was performed to determine the predictors. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Calibration curve was used to assess the concordance between predictive probabilities and true risks. RESULTS: The rate of GSU was 41.4%, whilst GS concordance rate was 44.4%. The independent predictors are prostate specific antigen (PSA), greatest percentage of cancer (GPC), clinical T-stage and Prostate Imaging Reporting and Data System (PI-RADS) score. Our model showed good discrimination (AUC of 0.735). Our model was validated internally with good calibration with bias-corrected C-index of 0.726. CONCLUSIONS: Utilization of basic clinical variables (PSA and T-stage) combined with imaging variable (PI-RADS) and pathological variable (GPC) could improve performance in predicting actual probabilities of GSU in the 24-core biopsy scheme. Our nomogram could help to assess the true risk and make optimal treatment decisions for PCa patients.
BACKGROUND: To explore the rate of Gleason sum upgrading (GSU) from biopsy to radical prostatectomy pathology and to develop a nomogram for predicting the probability of GSU in a Chinese cohort. METHODS: We retrospectively reviewed our prospectively maintained prostate cancer (PCa) database from October 2012 to April 2020. 198 patients who met the criteria were enrolled. Multivariable logistic regression analysis was performed to determine the predictors. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Calibration curve was used to assess the concordance between predictive probabilities and true risks. RESULTS: The rate of GSU was 41.4%, whilst GS concordance rate was 44.4%. The independent predictors are prostate specific antigen (PSA), greatest percentage of cancer (GPC), clinical T-stage and Prostate Imaging Reporting and Data System (PI-RADS) score. Our model showed good discrimination (AUC of 0.735). Our model was validated internally with good calibration with bias-corrected C-index of 0.726. CONCLUSIONS: Utilization of basic clinical variables (PSA and T-stage) combined with imaging variable (PI-RADS) and pathological variable (GPC) could improve performance in predicting actual probabilities of GSU in the 24-core biopsy scheme. Our nomogram could help to assess the true risk and make optimal treatment decisions for PCa patients.
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