Lei Li-Zhi, Xu Yi-Kai, Hou Mei-Rong, He Meng-Qi
Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. E-mail:
Nan Fang Yi Ke Da Xue Xue Bao. 2017 Aug 20;37(8):1092-1097. doi: 10.3969/j.issn.1673-4254.2017.08.15.
To assess the value of Prostate Imaging and Reporting and Data System: Version 2 (PI-RADS v2) combined with prostate specific antigen (PSA) in the diagnosis of peripheral zone (PZ) prostate cancer (PCa).
The preoperative magnetic resonance imaging and PSA data were ananlyzed for 69 patients with pathologically confirmed PCa and 109 non-PCa patients. PI-RADS v2 scores (1-5) was used to evaluate the risk of PZ PCa. The total PSA (tPSA) level, free to total PSA ratio (f/t PSA), PSA density (PSAD), PZ-PSAD and PI-RADS v2 scores were compared between the PCa and non-PCa patients. Logistic regression models were established with parameters that differed significantly the two groups. The receiver opearting characteristics (ROC) curve was constructed based on the P values derived from the logical regression models and PI-RADS scores to assess the diagnostic efficiency.
PI-RADS v2 score, tPSA, f/t PSA, PSAD and PZ-PSAD differed significantly between the two groups (P<0.01). Four predictive multivariate models were established: Logit P=-6.825+1.024PI-RADS v2+ 0.223tPSA (A), Logit P=-4.354+1.586PI-RADS v2-12.7841f/tPSA (B), Logit P=-8.993+1.630PI-RADS v2+17.091PSAD (C), and Logit P=-9.434+1.596PI-RADS v2+10.494PZ-PSAD (D), whose area under the ROC curves was 0.908, 0.891, 0.944, and 0.961, respectively, all significantly greater than that of PI-RADS v2 score (P<0.05).
Compared with PI-RADS v2 score alone, the combination of PI-RADS v2 score and PSA in the logistic regression model can improve the diagnostic efficiency of PZ PCa and offers better confidence in the decision of biopsy in suspected cases.
评估前列腺影像报告和数据系统第2版(PI-RADS v2)联合前列腺特异性抗原(PSA)在诊断外周带(PZ)前列腺癌(PCa)中的价值。
分析69例经病理证实的PCa患者和109例非PCa患者的术前磁共振成像和PSA数据。采用PI-RADS v2评分(1 - 5分)评估PZ PCa的风险。比较PCa组和非PCa组患者的总PSA(tPSA)水平、游离PSA与总PSA比值(f/t PSA)、PSA密度(PSAD)、PZ-PSAD和PI-RADS v2评分。建立包含两组间有显著差异参数的逻辑回归模型。基于逻辑回归模型和PI-RADS评分得出的P值构建受试者操作特征(ROC)曲线,以评估诊断效率。
两组间PI-RADS v2评分、tPSA、f/t PSA、PSAD和PZ-PSAD差异有统计学意义(P<0.01)。建立了四个预测多变量模型:Logit P = -6.825 + 1.024PI-RADS v2 + 0.223tPSA(A)、Logit P = -4.354 + 1.586PI-RADS v2 - 12.7841f/tPSA(B)、Logit P = -8.993 + 1.630PI-RADS v2 + 17.091PSAD(C)和Logit P = -9.434 + 1.596PI-RADS v2 + 10.494PZ-PSAD(D),其ROC曲线下面积分别为0.908、0.891、0.944和0.961,均显著大于PI-RADS v2评分(P<0.05)。
与单独的PI-RADS v2评分相比,逻辑回归模型中PI-RADS v2评分与PSA联合可提高PZ PCa的诊断效率,并为疑似病例的活检决策提供更高的可信度。