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  • Title: [Preliminary applicability evaluation of Prostate Imaging Reporting and Data System version 2 diagnostic score in 3.0T multi-parameters magnetic resonance imaging combined with prostate specific antigen density for prostate cancer].
    Author: Zuo MZ, Zhao WL, Wei CG, Zhang CY, Wen R, Gu YF, Li MJ, Zhang YY, Wu JF, Li X, Shen JK.
    Journal: Zhonghua Yi Xue Za Zhi; 2017 Dec 19; 97(47):3693-3698. PubMed ID: 29325321.
    Abstract:
    Objective: To investigate the preliminary applicability of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score in the condition of 3.0T multi-parametric magnetic resonance imaging (Mp-MRI) combined with clinical classic indicators for the diagnosis of prostate cancer (PCa). Methods: The clinical and MRI materials of 247 patients of suspicious prostate disease treated in Second Affiliated Hospital of Soochow University from June 2015 to November 2016 were analyzed retrospectively, including 110 cases with PCa and 137 cases without cancer.All cases underwent the high-resolution axial T(2)-weighted imaging (T(2)WI), diffusion weighted imaging (DWI) and dynamic contrast enhancement-magnetic resonance imaging (DCE-MRI) and were confirmed pathologically by puncture biopsies.The Mp-MRI materials of all cases were scored according to PI-RADS v2.The prostate volume and prostate specific antigen (PSA) density (PSAD) value were calculated according to the formulas.The univariate and multivariate analysis were performed for the observed indicators (age, prostate volume, PSA, PSAD and PI-RADS v2 score) to determine the independent predictors for PCa.Then, a Logistic regression model (combined prediction model) was established by the independent predictors for combined diagnosis of PCa.The receiver operating characteristic curve (ROC) curve analysis was performed to get the sensitivity and specificity of each independent predictor and the model to diagnose PCa.The differences of AUC values of each independent predictor and the model were compared with each other to evaluate the diagnostic performance for PCa. Results: The differences in the age, prostate volume, PSA, PSAD and the PI-RADS v2 score between patients with PCa and non-cancer group were all statistically significant (t=2.870, Z=-4.230, -7.787, -9.477, -10.826, all P<0.05). The PSAD and PI-RADS v2 score were independent predictors for PCa (OR=3.331, 10.546, both P<0.05). The Logistic regression combined prediction model by PI-RADS v2 score and PSAD to forecast PCa was Logit(P)=-5.097+ 2.309×PSAD+ 1.214×PI-RADS v2 score.The area under the curve (AUC) of ROC in the combined model (0.911) was higher than that in the PI-RADS v2 score (0.886) and PSAD (0.851) and the differences were all statistically significant (Z=2.416, 2.716, both P<0.05); but the difference in the AUC value between PI-RADS v2 score and PSAD was not statistically significant (Z=1.191, P=0.234). The diagnostic sensitivity of PSAD, PI-RADS v2 score and the model were: 0.891, 0.782 and 0.855, respectively; the specificity were 0.449, 0.912 and 0.847, respectively on their positive thresholds (0.15 μg·L(-1)·ml(-1,) 4 and -0.82). Conclusion: PI-RADS v2 score combined with PSAD in diagnosing PCa is superior to the single application of them and it can lead to high diagnostic sensitivity and specificity for PCa. 目的： 初步评价3.0T多参数磁共振成像(Mp-MRI)第二版前列腺影像报告和数据系统(PI-RADS v2)诊断评分联合临床常用指标对前列腺癌(PCa)的临床诊断价值。 方法： 回顾分析2015年6月至2016年11月247例在苏州大学附属第二医院经穿刺活检病理证实并行高分辨率T(2)加权像(T(2)WI)、轴位弥散加权成像(DWI)和动态对比增强(DCE)-MRI的疑诊前列腺病变患者的临床和MRI影像资料。其中，PCa 110例，非癌137例。对入组病例的Mp-MRI图像进行回顾性PI-RADS v2诊断评分，并根据公式计算前列腺体积和前列腺特异性抗原(PSA)密度(PSAD)；对入组病例各观察指标进行单因素和多因素分析，得出PCa的独立预测指标；建立独立预测指标联合预测PCa的Logistic回归模型(联合预测模型)；通过受试者工作特征曲线(ROC曲线)分析分别得出独立预测指标和联合预测模型对PCa诊断敏感度和特异度，两两比较ROC曲线下面积(AUC)，评价诊断效能。 结果： 入组病例的年龄、前列腺体积、PSA、PSAD和PI-RADS v2评分在癌组与非癌组间差异均有统计学意义(t＝2.870，Z＝－4.230、－7.787、－9.477、－10.826，均P<0.05)。PI-RADS v2评分和PSAD是PCa的独立预测指标(OR＝3.331、10.546，均P<0.05)。PI-RADS v2评分与PSAD联合预测PCa的Logistic回归模型为：Logit(P)＝－5.097＋2.309×PSAD＋1.214×PI-RADS v2评分。联合预测模型的AUC值(0.911)大于PI-RADS v2评分(0.886)和PSAD(0.851)，差异均有统计学意义(Z＝2.416、2.716，均P<0.05)，PI-RADS v2评分与PSAD间AUC值差异无统计学意义(Z＝1.191，P＝0.234)；PSAD、PI-RADS v2评分和联合预测模型分别取阈值0.15 μg·L(－1)·ml(－1)、4分和－0.82时对PCa的诊断敏感度分别为0.891、0.782和0.855，特异度分别为0.449、0.912和0.847。 结论： 3.0T Mp-MRI PI-RADS v2诊断评分和PSAD联合应用对PCa的诊断效能优于单独应用，同时达到了较高的诊断敏感度和特异度。.
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