基于PI-RADS v2评分的Mp-MRI联合PSAD对中高级别前列腺癌预测价值的分析

目的 探讨基于第2版前列腺影像报告和数据系统（PI-RADS v2）评分的多参数磁共振成像（Mp-MRI）联合前列腺特异性抗原密度（PSAD）对中高级别前列腺癌（MHGPCa）的预测价值。方法 回顾性分析227例经穿刺活检病理证实并行高分辨率T2WI联合DWI和DCE MRI的疑诊前列腺病变患者的临床资料和MRI影像资料，其中MHGPCa 70例、低级别前列腺癌（LGPCa）23例和非癌134例。根据PI RADS v2评分标准对入组病例的MRI图像进行回顾性评分，比较MHGPCa与非MHGPCa患者的年龄、前列腺体积、总前列腺特异性抗原、游离前列腺特异性抗原／总前列腺特异性抗原、PSAD和PI-RADS v2评分并进行多因素分析以筛选MHGPCa的独立预测指标；建立独立预测指标联合预测MHGPCa的Logistic回归模型并采用ROC曲线评价诊断效能。结果 多因素分析显示，PI-RADS v2评分和PSAD是MHGPCa的独立预测指标（P＜0.05）；两者联合预测MHGPCa的Logistic回归模型为Logit（P）=-5.514+0.7×PSAD+1.219×PI-RADS v2评分。模型的ROC曲线下面积（AUC）为0.916，高于PI-RADS v2评分的0.882和PSAD的0.892，且与PI-RADS v2评分间的差异有统计学意义（P＜0.05），但与PSAD间的差异无统计学意义（P＞0.05）；PI-RADS v2评分与PSAD间AUC值的差异无统计学意义（P＞0.05）。结论 基于PI-RADS v2评分的Mp-MRI和PSAD联合应用对MHGPCa的诊断效能优于PI-RADS v2评分的单独应用，可考虑在临床中推广。

Analysis of Mp-MRI based on PI-RADS v2 score combined with PSAD for middle-high grade prostate cancer

Objective To analyze the value of multi-parameter magnetic resonance imaging （Mp-MRI） based on Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） score combined with prostate specific antigen density （PSAD） for middle high grade prostate cancer（MHGPCa）. MethodsThis retrospective study analyzed the datas of 227 patients of suspicious prostate disease， including 70 cases of MHGPCa， 23 cases of LGPCa and non-cancer of 134 cases who were all confirmed pathologically by puncture biopsies and underwent the high-resolution axial T2WI combined with DWI and DCE-MRI. The MRI images of the 227 patients were scored according to the PI-RADS v2 and the prostate volume and PSAD value were calculated. The univariate and multivariate analysis were performed for the observed indicators， including age， prostate volume， total prostate specific antigen （tPSA）， free-to-total PSA ratio （f／tPSA） and PI-RADS v2 score， to determine the independent predictors for MHGPCa. Then， the Logistic regression model was established using the independent predictors to jointly predict MHGPCa. The receiver operating characteristics （ROC） curves of the independent predictors and the model to diagnose MHGPCa were drew respectively to get the best threshold， and the differences of AUC values were compared to evaluate the diagnostic performance for MHGPCa. Results Among all the observed indicators， PI-RADS v2 score and PSAD were independent predictors for MHGPCa （P＜0.05）. The Logistic regression model established by PI-RADS v2 score combined with the PSAD to predict MHGPCa was as follows： Logit（P）=-5.514+0.7×PSAD+1.219×PI-RADS v2 score. The area under curve （AUC） value of the model （0.916） was higher than those of the PI-RADS v2 score and PSAD（0.882 and 0.892） and the differences between the model and PI RADS v2 score was statistically significant （P＜0.05）；but the difference of the AUC value between the model and PSAD was not statistically significant （P＞0.05） and the difference between PI-RADS v2 score and PSAD was not statistically significant （P＞0.05）. Conclusion The diagnositic performance of Mp MRI based on PI-RADS v2 score combined with PSAD for MHGPCa is superior to that of PI RADS v2 score alone. Combined application of PI-RADS v2 score and PSAD can be popularized in clinical practice.