Hu Wei, Guo ShiKuan, Meng XiangLiang, Wang FuLi, Ma ShuaiJun, Zhang Chao, Wang JingYi, Yuan Lei, Zhang LongLong, Jing YuMing, Chen Jian, Hou HaoZhong, Wang Yang, Zhang KeYing, Li Yu, Kang Fei, Han DongHui, Guo HongQian, Zhang JingLiang, Ren Jing, Qin WeiJun
Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xi'an, 710032, Shaanxi, China.
Department of Urology, No.967th Hospital of Joint Logistic Support Force of PLA, Dalian, Liaoning, China.
Sci Rep. 2025 Jul 28;15(1):27453. doi: 10.1038/s41598-025-12312-z.
Multi-parametric magnetic resonance imaging (mpMRI) is a valuable medical technology for detecting clinically significant prostate cancer (csPCa). The diagnostic accuracy of mpMRI for csPCa in negative mpMRI (PI-RADS 1-2) remains suboptimal, underscoring the need for improvements for csPCa. This study aimed to build a visual predictive nomogram for early detection of csPCa in negative mpMRI. We retrospectively reviewed 303 men from our institution who simultaneously underwent Ga-PSMA-11 PET/CT and mpMRI before a biopsy between March 2020 and July 2022 and 130 men from the outside institution (Nanjing Drum Tower Hospital) as external validation between September 2021 and June 2022. The enrolled patients in our institution were randomly divided into the training set (n = 212) and the internal validation set (n = 91). Multivariate logistic regression was performed to identify independent predictors and establish a nomogram using SUVmax of Ga-PSMA-11 PET/CT and prostatic specific antigen density (PSAD) to predict the occurrence of csPCa in negative mpMRI. Multivariate logistic regression demonstrated that SUVmax (odds ratio [OR] 5.296, 95% confidence interval [CI] 1.691-23.972), and PSAD (OR 4.867, 95%CI 2.389-10.901) were independent predictors for csPCa in negative mpMRI. The area under the curve (AUC) of the nomogram was 0.819 (95%CI 0.729-0.890). Additionally, both the decision curve analysis (DCA) curve and the net reclassification improvement (NRI) showed significant improvements for csPCa in our model. External validation validated the reliability of the prediction nomogram. The visual interactive web risk calculator PI-RADS/SUVmax/PSAD model (PSP Model, www.cspca.online ) based on the nomogram allows us to assess the risk of having csPCa. The nomogram based on preoperative examination was developed to predict csPCa in negative mpMRI and help reduce unnecessary biopsies. The visual PSP Model is an effective and accurate tool for urologists to use in the early prediction and timely management of csPCa.
多参数磁共振成像（mpMRI）是检测具有临床意义的前列腺癌（csPCa）的一项重要医学技术。mpMRI对阴性mpMRI（前列腺影像报告和数据系统[PI-RADS] 1-2类）中csPCa的诊断准确性仍不理想，这突出表明需要改进csPCa的检测方法。本研究旨在构建一种视觉预测列线图，用于在阴性mpMRI中早期检测csPCa。我们回顾性分析了2020年3月至2022年7月间在我院同时接受镓[Ga]-前列腺特异性膜抗原（PSMA）-11正电子发射断层扫描/计算机断层扫描（PET/CT）和mpMRI检查，随后进行活检的303名男性患者，以及2021年9月至2022年6月间来自外部机构（南京鼓楼医院）的130名男性患者作为外部验证组。我院纳入的患者被随机分为训练集（n = 212）和内部验证集（n = 91）。进行多因素逻辑回归分析以确定独立预测因素，并使用Ga-PSMA-11 PET/CT的最大标准摄取值（SUVmax）和前列腺特异性抗原密度（PSAD）建立列线图，以预测阴性mpMRI中csPCa的发生情况。多因素逻辑回归分析显示，SUVmax（比值比[OR] 5.296，95%置信区间[CI] 1.691-23.972）和PSAD（OR 4.867，95%CI 2.389-10.901）是阴性mpMRI中csPCa的独立预测因素。列线图的曲线下面积（AUC）为0.819（95%CI 0.729-0.890）。此外，决策曲线分析（DCA）曲线和净重新分类改善（NRI）均显示我们模型中csPCa有显著改善。外部验证证实了预测列线图的可靠性。基于该列线图的视觉交互式网络风险计算器PI-RADS/SUVmax/PSAD模型（PSP模型，www.cspca.online）使我们能够评估患csPCa的风险。基于术前检查开发的列线图用于预测阴性mpMRI中的csPCa，并有助于减少不必要的活检。视觉PSP模型是泌尿外科医生用于csPCa早期预测和及时管理的有效且准确的工具。
