Department of Urology, Capital Medical University Affiliated Beijing Friendship Hospital, No. 95, Yongan Road, Xicheng District, Beijing, People's Republic of China.
BMC Urol. 2021 Jan 6;21(1):3. doi: 10.1186/s12894-020-00773-5.
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.
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.
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.
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.
探讨从活检到根治性前列腺切除术病理的 Gleason 总和升级（GSU）率，并为中国队列开发预测 GSU 概率的列线图。
我们回顾性地审查了我们从 2012 年 10 月至 2020 年 4 月前瞻性维护的前列腺癌（PCa）数据库。纳入了符合标准的 198 名患者。采用多变量逻辑回归分析确定预测因素。基于独立预测因素构建列线图。进行接收者操作曲线以评估鉴别力。校准曲线用于评估预测概率与真实风险之间的一致性。
GSU 率为 41.4%，而 GS 一致性率为 44.4%。独立预测因素是前列腺特异性抗原（PSA）、最大癌症百分比（GPC）、临床 T 分期和前列腺影像报告和数据系统（PI-RADS）评分。我们的模型显示出良好的鉴别力（AUC 为 0.735）。我们的模型在内部进行了验证，具有良好的校准，偏置校正的 C 指数为 0.726。
利用基本临床变量（PSA 和 T 分期）结合影像学变量（PI-RADS）和病理学变量（GPC）可以提高在 24 芯活检方案中预测实际 GSU 概率的性能。我们的列线图可以帮助评估真实风险并为 PCa 患者做出最佳治疗决策。
