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目的:通过分析经直肠前列腺超声影像学特点及部分临床特征建立前列腺结节恶性风险预测模型,提高穿刺阳性率,减少不必要的前列腺穿刺。方法:回顾2010年1月~2014年1月在我院行前列腺穿刺活检且经直肠超声(TRUS)发现前列腺结节的患者151例。收集总前列腺特异性抗原(tPSA)、游离前列腺特异性抗原(fPSA)、f/t比值(fPSA/tPSA)、前列腺体积(PV)、PSA密度(PSAD)与经直肠前列腺B超发现的前列腺结节的大小、结节回声、结节位置、边缘是否清晰等资料,采用Logistic单因素分析寻找前列腺癌独立预测因子,Logistic多因素分析制定前列腺结节恶性风险预测模型。结果:142例患者入选,共170个结节纳入最终分析,其中恶性结节58例。单因素分析中结节位置、回声、边界是否清晰、lgPV、lgtPSA、f/t比值在恶性结节及良性结节组的分布差异有统计学意义,多因素分析中边界、lgtPSA、lgPV进入最终的风险预测模型。logit(前列腺癌)=4.081-1.090×边界-2.941×lgPV+1.836×lgtPSA。结论:本研究中受试者工作特征曲线下面积0.800,与国内外同类研究总体准确性相近,具有一定的准确性。
OBJECTIVE: To establish a malignant prostate cancer risk prediction model by analyzing the characteristics of transrectal ultrasound and some clinical features to improve the positive rate of pricking and reduce unnecessary prostatic puncture. Methods: A total of 151 patients with prostatic nodules detected by transrectal ultrasound (TRUS) were retrospectively reviewed from January 2010 to January 2014 in our hospital. Collecting total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), f / t ratio (fPSA / tPSA), prostate volume (PV), PSA density (PSAD) and prostate hyperplasia Section size, nodule echo, nodule location and edge are clear and so on. Logistic univariate analysis was used to find independent predictors of prostate cancer, and Logistic multivariate analysis was used to develop a malignant prostate cancer risk prediction model. Results: A total of 142 patients were enrolled. A total of 170 nodules were included in the final analysis, of which 58 were malignant nodules. The distributions of lgPV, lgtPSA and f / t in malignant nodules and benign nodules were statistically significant in univariate analysis. The borderline, lgtPSA and lgPV in the multivariate analysis were the most significant The risk prediction model. logit (Prostate cancer) = 4.081-1.090 × boundary -2.941 × lgPV + 1.836 × lgtPSA. CONCLUSIONS: The area under the receiver operating characteristic curve for this study was 0.800, which is similar to the overall accuracy of similar studies at home and abroad with a certain degree of accuracy.