摘要： 目的 探讨前列腺影像报告和数据系统(PI-RADS)v2.1联合前列腺特异性抗原(PSA)相关参数对临床显著性前列腺癌的诊断效能,并进行内部验证。方法 收集2016年1月至2021年4月北部战区总医院放射诊断科150例活检前接受前列腺磁共振检查且总前列腺特异抗原(tPSA)>4ng/mL患者的临床资料。Gleason评分≥7分患者纳入临床显著性前列腺癌(csPCa)组(n=71),Gleason评分<7分及良性疾病(前列腺增生、前列腺炎)患者纳入非csPCa组(n=79)。从2组中按7:3比例随机分配至建模组和验证组。采用t检验比较2组PI-RADS v2.1、tPSA、游离PSA/tPSA(f/tPSA),PSA密度(PSAD)的差异,将有统计学差异(P<0.05)指标作为自变量,建立PI-RADS v2.1与PSA相关参数的logistic预测模型:(1)PI-RADS v2.1+tPSA;(2)PI-RADS v2.1+t/fPSA;(3)PI-RADS v2.1+PSAD。将验证组数据代入模型方程,以预测概率(P)及PI-RADS v2.1绘制受试者操作特征(ROC)曲线,评估诊断效能。结果 PI-RADS v2.1与PSA相关参数建立预测模型:(1)Logit P=-7.313+1.62PI-RADS v2.1+0.088tPSA;(2)LogitP=-0.453+1.833PI-RADS v2.1-39.811f/tPSA;(3)Logit P=-12.031+1.917PI-RADS v2.1+29.206PSAD。模型(1)、(2)、(3)的预测概率(P)与PI-RADS v2.1的ROC曲线下面积分别为0.927、0.915、0.984和0.899;Z检验进行两两比较结果显示,模型(1)、(2)与PIRADS v2.1诊断csPCa比较差异无统计学意义(均P>0.05);模型(3)与PI-RADS v2.1诊断csPCa的差异有统计学意义(P<0.05)。结论 PI-RADS v2.1与PSAD联合诊断的预测模型对csPCa的诊断效能高于PI-RADS v2.1单独诊断。

PI-RADS v2.1联合前列腺特异性抗原相关参数诊断临床显著性前列腺癌的预测模型及内部验证

钟宇, 田芳, 周姝, 邹明宇, 张立波, 刘文源

北部战区总医院放射诊断科, 沈阳 110016

收稿日期:2022-03-21出版日期:2022-12-30发布日期:2022-12-12
通讯作者:刘文源E-mail:liuwenyuan2000@163.com
作者简介:钟宇 (1985-)，女，主治医师，硕士研究生.
基金资助:辽宁省自然科学基金(201602781)


关键词: PI-RADS v2.1, 磁共振成像, 临床显著性前列腺癌, 前列腺特异性抗原, 联合诊断
Abstract: Objective To evaluate the diagnostic efficacy of PI-RADS v2.1 combined with prostate-specific antigen(PSA)-related parameters for clinically significant prostate cancer(csPCa) and to perform an internal validation. Methods The clinical data of 150 patients who underwent prostate magnetic resonance before biopsy and had total prostate-specific antigen(tPSA) >4 ng/mL were evaluated from January 2016 to April 2021 at the Department of Diagnostic Radiology， Northern War Zone General Hospital. Patients with a Gleason score of ≥7 were included in the csPCa group(n = 71)， and patients with a Gleason score of<7 and patients with benign disease(prostatic hyperplasia or prostatitis) were included in the non-csPCa group(n = 79). The two groups were randomly assigned to the modeling and validation groups in a 7 ∶ 3 ratio. t test was used to compare the differences in PI-RADS v2.1， tPSA， free PSA/total PSA (f/tPSA)， and PSA density(PSAD) between the two groups， and statistically different(P<0.05) indicators were used as independent variables to establish a logistic prediction model of PI-RADS v2.1 and PSA-related parameters：(1) PI-RADS v2.1+tPSA；(2) PI-RADS v2.1+t/fPSA； and(3) PI-RADS v2.1+PSAD. Data from the validation group were substituted into the model equation， and the receiver operating characteristic(ROC) curves were plotted with predicted probability(P) and PI-RADS v2.1 to assess the diagnostic efficacy. Results PI-RADS v2.1 was modeled with PSA-related parameters to predict(1) Logit P =-7.313+1.62PI-RADS v2.1+0.088tPSA；(2) Logit P =-0.453+1.833PI-RADS v2.1-39.811f/tPSA； and(3) Logit P =-12.031+1.917PI-RADS v2.1+29.206PSAD. The area under the ROC curves of the predicted probabilities P of models(1)， (2)， and(3) with PI-RADS v2.1 was 0.927， 0.915， 0.984， and 0.899， respectively， and the results of the two-way comparisons performed by Z test showed that models(1)， (2)， and PI-RADS v2.1 were not statistically significant(all P > 0.05). The differences between model(3) and PI-RADS v2.1 for the diagnosis of csPCa were statistically significant(P<0.05). Conclusion The predictive model of PI-RADS v2.1， combined with PSAD， has higher diagnostic efficacy for csPCa than PI-RADS v2.1 alone.
Key words: PI-RADS v2.1, magnetic resonance imaging, clinically significant prostate cancer, prostate-specific antigen, combined diagnosis
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