建立预测中国人前列腺重复穿刺活检阳性的数学模型

目的:建立可以预测国人经直肠超声引导下重复穿刺活检阳性的数学模型。方法:170例在首次穿刺活检诊断为前列腺良性病变的患者行重复穿刺。记录并分析患者的年龄、前列腺体积、血清PSA、游离PSA(f-PSA)/总PSA(t-PSA)、PSA上升速度、PSA密度(PSAD)、直肠指检(DRE)、首次穿刺病理结果等相关因素。将变量通过逐步回归建立回归方程,在此基础上建立重复穿刺活检阳性的危险评分数学模型。该模型的预测价值通过受试者工作曲线下面积来评估。结果:170例前列腺重复穿刺活检的患者中,前列腺癌的穿刺检出率为31.8%(54/170)。建立的数学模型影响因素包括:患者的年龄、前列腺体积、PSA、f-PSA/t-PSA、PSA上升速度、PSAD、DRE、首次穿刺结果是否为上皮内瘤变。该模型预测价值较高,曲线下面积为82.4%,大于患者PSAD、前列腺体积、PSA上升速度、f-PSA/t-PSA、DRE等单因素的66.9%、72.6%、69.6%、69.3%、58.5%。结论:该数学模型是临床多因素综合分析基础上建立的,可以很好地预测前列腺重复穿刺活检阳性的概率。

Establishment of a nomogram for predicting positive repeat prostate biopsy in Chinese men

Objective: To develop a nomogram for predicting the probability of prostate cancer at transrectal ultrasound-guided repeat prostate biopsy in Chinese men.Methods: We performed repeat biopsy for 170 patients with benign prostate diseases diagnosed on the first biopsy,and analyzed the correlation of positive repeat biopsy with age,prostate volume,PSA,free-to-total PSA(f-PSA/t-PSA),PSA velocity,PSA density,results of digital rectal examination(DRE) and previous histology.We entered the variables stepwise into logistic regression models,and established a nomogram for the risk score on the probability of positive repeat biopsy,whose predictive value was assessed by receiver operating characteristic(ROC) analysis.Results: Prostate cancer was detected in 31.8% of the repeat biopsies(54/170).The most accurate predictive nomogram comprised age,PSA,f-PSA/t-PSA,PSA velocity,prostate volume,DRE and previous prostatic intraepithelial neoplasia(PIN) findings.The nomogram exhibited a high predictive value,with the area under the ROC curve(AUC) of 82.4%,significantly greater than that of the prediction based on PSA density(AUC: 66.9%),prostate volume(AUC: 72.6%),PSA velocity(AUC: 69.6%),f-PSA/t-PSA(AUC: 69.3%),or DRE(AUC: 58.5%) alone.Conclusion: The nomogram is an accurate multi-variable predicting tool to determine the probability of positive repeat prostate biopsy.