列线图
医学
前列腺癌
队列
前列腺特异性抗原
接收机工作特性
直肠检查
逻辑回归
活检
前列腺
泌尿科
前列腺活检
曲线下面积
放射科
癌症
核医学
肿瘤科
内科学
作者
Can Hu,Jiale Sun,Zhenyu Xu,Zhiyu Zhang,Qi Zhou,Jiangnan Xu,Hao Chen,Chao Wang,Jun Ouyang
摘要
Abstract Objective To develop and externally validate a novel nomogram in biopsy‐naïve patients with prostate‐specific antigen (PSA) <10 ng/ml and PI‐RADS v2.1 = 3 lesions. Methods We retrospectively collected 307 men that underwent initial biopsy from October 2015 to January 2022 in Cohort 1 (The First Affiliated Hospital of Soochow University). External cohort (Cohort 2, Kunshan Hospital) included 109 men that met our criteria from July 2016 to June 2021. By Slicer‐3D Software, the volume of all lesions was divided into two subgroups (PI‐RADS v2.1 = 3a and 3b). Logistic regression analysis was performed to screen for variables and construct nomogram by analyzing clinical data from Cohort 1. Receiver operating characteristics curve analysis, calibration plot and decision curve analysis (DCA) were plotted to validate the nomogram in external cohort. Results A total of 70 (22.8%) patients was diagnosed with prostate cancer in Institution 1. Among them, 34 (11.1%) had clinically significant prostate cancer (csPCa). Age, prostate‐specific antigen density, digital rectal examination, PI‐RADS v2.1 = 3 subgroups (3a and 3b) and apparent diffusion coefficient (ADC, <750 mm 2 /s) were predictive factors for prostate cancer (PCa) and csPCa. High area under the curve of the nomogram was found in Cohort 1 and Cohort 2 for PCa (0.857 vs. 0.850) and for csPCa (0.896 vs. 0.893). Calibration curves showed excellent agreement between the predicted probability and actual risk for the models in internal and external validation. The DCA demonstrated net benefit of our nomogram. Conclusion Until now, this is the first nomogram that predicts PCa and csPCa in biopsy‐naïve patients with PSA <10 ng/ml and PI‐RADS v2.1 = 3 lesions. Furthermore, PI‐RADS v2.1 = 3 subgroups were considered to be an independent risk factor in our model. Our nomogram may assist urologists in biopsy decision making for these so‐called “double gray zone” patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI