经直肠超声造影参数及临床资料预测前列腺癌的多因素分析

Multivariate analysis of prediction prostate cancer based on the contrast-enhanced transrectal ultrasonography and clinical data

目的探讨经直肠超声造影参数及临床相关资料等多因素预测前列腺癌的价值。方法回顾分析2013年3月至2017年3月152例临床怀疑前列腺癌患者的临床资料，包括年龄、前列腺体积、血清前列腺特异抗原（prostate specific antigen，PSA）等。所有患者均于术前行经直肠超声造影（transrectal contrast enhanced ultrasound，TR-CEus）并经直肠超声引导下穿刺活检，采用QLab软件绘制时间强度曲线，定量分析上升时间（RT）、达峰时间（TTP）、峰值强度（PI）、曲线下面积（AUC）等6个参数，根据病理结果分为前列腺增生（benign prostatic hyperplasia，BPH）组和前列腺癌组，以病理结果是否为前列腺癌为因变量，以P〈0．30的变量为自变量，采用Logistic回归分析，以有统计学意义的因素建立诊断模型，构建ROC曲线，计算曲线下面积和诊断界值。结果152例病例中前列腺癌69例，BPH83例，单因素分析中，前列腺癌组TR—CEUS参数中的RT低于BPH组（P=0．021），PI高于BPH组（P=0．005）。将单因素分析PdO．30的变量包括年龄、体积、PSA、RT、PI、MTT、AUC和TTP共8个因素纳入Logistic回归中，结果显示PSA、PI和AUC是预测前列腺癌的独立危险因素（P〈0．05），以此建立诊断模型，计算ROC曲线下面积为0．797（P〈0．001），诊断模型界值为0．383，预测前列腺癌的敏感性为77．9％，特异性为79．5％，约登指数57．5％，阳性预测值65．1％，阴性预测值81．5％。结论TR—CEUS参数中PI和AUC是预测前列腺癌的独立危险因素，与血清PSA联合建立诊断模型对预测前列腺癌有一定临床应用价值。

Objective To explore the value of transrectal contrast-enhanced ultrasonography（TR- CEUS） and clinical data in prediction prostate cancer （PCa）. Methods The clinical and images data of 152 patients who highly suspected with PCa were analyzed retrospectively. All patients were not treated before operation,in addition, TR-CEUS and prostate biopsy were taken. To analyze images with time intensity curves（TIC） analysis software, 6 parameters including arrival time（RT）, time-to-peak（TTP）, peak intensity （PI） and so on were measured. According to the results of pathological findings and images, the patients were classified into PCa and benign prostatic hyperplasia（BPH） group. The pathological result was used as the dependent variable, the P d0.30 variables as independent variables, multivariate analysis was performed using Logistic regression, the statistically significant factors were used to establish a diagnosis model, construct ROC curve and calculate the area under the curve （AUC）. Results Of total 152 patients, BPH accounted for 54.6X （83/152）, PCa accounted for 45.4% （69/152）. In the single factor analysis, the RT of TR-CEUS parameter in PCa group was lower than that in BPH group （ P = 0.021）,and the PI was higher than that in BPH group （ P = 0.005）. The single factor analysis of P 〈 0.30 variables including age, volume,PSA, RT, PI, MTT, AUC and TTP eight variables were used in Logistic regression, the results showed that PSA, PI and AUC were independent risk factors in prediction PCa （ P 〈0.05）, established a diagnosis model. The area under ROC curve was 0.797 （P 〈0.001）, the diagnosis model of boundary value was 0. 383, the PCa forecast sensitivity was 77.9 %, specificity was 79.5 %, Youden index was 57.5 %, the positive predictive value was 65.1 %, and the negative predictive value was 81.5 %. Conclusions In the TR-CEUS parameter, PI and AUC are independent predictors in prediction PCa, and the establishment of a diagnostic model combined with serum PSA has a certain clinical value in predicting PCa.