结合PI-RADS V2.1、MRI定量参数和临床指标的临床显著前列腺癌列线图预测模型的开发与验证：一项双中心研究
Development and validation of a nomogram prediction model for clinically significant prostate cancer combined with PI-RADS V2.1, MRI quantitative parameters and clinical indicators: a two-center study.
作者信息
Chen Yunhui, Yan Long, Xianmei Jiang, Heyi Gu, Wei Xie, Chao Peng, Yanwen Dong, Shicun Dong, Chao Gao, Cui Yu, Peng Gu, Xiaodong Liu, Xiaoyu Tuo, Bingbing Ling, Wenqing Ji, Kexian Gao, Qingqing Li, Linglin Zheng, Yun Zhu, Lei Zhao, Jihong Hu, Wei Zhao, Yaying Yang, Juan Hu
机构信息
Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Medical Imaging Department, Gejiu People's Hospital, Gejiu, Yunnan, China.
出版信息
Front Oncol. 2024 Nov 22;14:1467793. doi: 10.3389/fonc.2024.1467793. eCollection 2024.
OBJECTIVE
To develop and validate a multi-index nomogram prediction model for clinically significant prostate cancer(CSPCa) by combining the PI-RADS V2.1, quantitative magnetic resonance imaging (MRI) parameters and clinical indicators.
METHODS
A total of 1740 patients (75% in the derivation cohort and 25% in the internal validation cohort) and 342 patients (the external validation cohort) were retrospectively included in the MRI follow-up database of the First Affiliated Hospital of Kunming Medical University between January 2015 and April 2021,and Gejiu People's Hospital between January 2020 and December 2022.Important predictors of CSPCa in MRI-related quantitative parameters, PSA-derived indicators, and clinical indicators, such as age, were screened. The Net Reclassification Improvement Index(NRI),Integrated Discrimination Improvement Index(IDI), and clinical decision curve analysis (DCA) were calculated to compare the performances of the different models. Receiver operating characteristic(ROC) curves and clinical calibration curves were used to analyze and compare diagnostic effects.
RESULTS
The AUC value, best cut-off value, specificity, sensitivity and accuracy of model 1(PI-RADS + PSAD) derivation cohort were 0.935, 0.304, 0.861, 0.895 and 0.872, respectively. The AUC values of the internal and external validation cohorts for model 1 were 0.956 and 0.955, respectively. The AUC value, best cut-off value, specificity, sensitivity and accuracy of model 2(PI-RADS +PSAD + ADCmean) derivation cohort were 0.939, 0.401, 0.895, 0.853 and 0.882, respectively. The AUC values of the internal and external validation cohorts for model 2 were 0.940 and 0.960,respectively. After adding the ADCmean to the model, the NRI(categorical), NRI(continuous) and IDI values were 0.0154, 0.3498 and 0.0222, respectively. There was no significant difference between the predicted probability and actual probability (p> 0.05).
CONCLUSION
Models 1 and 2 had reliable, efficient and visual predictive value for CSPCa. The ADCmean is an important predictive indicator.
目的
通过整合前列腺影像报告和数据系统（PI-RADS）V2.1、定量磁共振成像（MRI）参数及临床指标，构建并验证用于临床显著性前列腺癌（CSPCa）的多指标列线图预测模型。
方法
回顾性纳入2015年1月至2021年4月昆明医科大学第一附属医院以及2020年1月至2022年12月个旧市人民医院MRI随访数据库中的1740例患者（75%纳入推导队列，25%纳入内部验证队列）和342例患者（外部验证队列）。筛选MRI相关定量参数、前列腺特异抗原（PSA）衍生指标及年龄等临床指标中CSPCa的重要预测因子。计算净重新分类改善指数（NRI）、综合判别改善指数（IDI）和临床决策曲线分析（DCA），以比较不同模型的性能。采用受试者操作特征（ROC）曲线和临床校准曲线分析并比较诊断效果。
结果
模型1（PI-RADS + PSAD）推导队列的曲线下面积（AUC）值、最佳截断值、特异性、敏感性和准确性分别为0.935、0.304、0.861、0.895和0.872。模型1内部验证队列和外部验证队列的AUC值分别为0.956和0.955。模型2（PI-RADS + PSAD + 平均表观扩散系数[ADCmean]）推导队列的AUC值、最佳截断值、特异性、敏感性和准确性分别为0.939、0.401、0.895、0.853和0.882。模型2内部验证队列和外部验证队列的AUC值分别为0.940和0.960。在模型中加入ADCmean后，NRI（分类）、NRI（连续）和IDI值分别为0.0154、0.3498和0.0222。预测概率与实际概率之间无显著差异（p>0.05）。
结论
模型1和模型2对CSPCa具有可靠、高效且直观的预测价值。ADCmean是一个重要的预测指标。
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