建立前列腺特异性抗原密度联合PI-RADS v2.1评分对临床有意义前列腺癌的预测模型  

Combination of prostate specific antigen density and PI-RADSv 2.1 score predicts the clinically significant prostate cancer

作　　者：胡潇 陆洪兵 郑鹏 潘中健 HU Xiao;LU Hong-bing;ZHENG Peng(Department of Urology,The First People's Hospital of Zhenjiang,Zhenjiang 212000,China)

机构地区：[1]镇江市第一人民医院泌尿外科,212000

出　　处：《中国实用医药》2025年第7期14-18,共5页China Practical Medicine

摘　　要：目的通过建立前列腺特异性抗原密度(PSAD)联合前列腺影像报告及数据分析系统2.1版(PI-RADS v2.1)评分对临床有意义前列腺癌(CsPCa)的预测模型,提升前列腺穿刺活检的阳性率并减少不必要的穿刺,优化临床的穿刺策略。方法回顾性分析经直肠或经会阴前列腺穿刺活检者的临床资料,包括年龄、身高、体重等一般情况,前列腺特异性抗原(PSA),前列腺体积(PV),PI-RADS v2.1评分及活检的病理结果。经单因素和多因素Logistic回归分析筛选出CsPCa的影响因素,并建立不同PSA区间的PSAD联合PI-RADS v2.1评分的预测模型,通过绘制受试者工作特征(ROC)曲线及估算其曲线下面积(AUC)评估模型对CsPCa的诊断效能。结果根据纳入标准和排除标准,共收集了282例患者。分析发现无论血清PSA值如何,PSAD和PI-RADS v2.1评分均是CsPCa的独立危险因素(P<0.01)。在PSA<10 ng/ml区间中,PSAD和PI-RADS v2.1联合预测模型的阴性预测值达96.1%;在PSA≥10 ng/ml区间中,分别绘制了PSA、PI-RADS v2.1评分、PI-RADS v2.1评分+PSA、PI-RADS v2.1评分+PSAD为变量的ROC曲线,其AUC值分别为0.711、0.812、0.828、0.862,提示PI-RADS v2.1评分+PASD预测模型显著优于单一PSA(Z=4.047,P=0.000<0.05)、单一PI-RADS v2.1评分(Z=3.275,P=0.001<0.05)以及PI-RADS v2.1评分+PSA预测模型(Z=2.658,P=0.008<0.05)。结论PSAD与PI-RADS v2.1评分建立的联合模型能更好地预测CsPCa的发生,尤其是当PSA<10 ng/ml时,联合模型能有效避免部分患者的过度诊断和治疗,为临床的穿刺决策带来更优的选择。Objective To establish a novel predictive model for clinically significant prostate cancer(CsPCa),combining prostate specific antigen density(PSAD)and prostate imaging reporting and data system(PI-RADS v2.1)score,for improving positive rate of prostate biopsy and reducing unnecessary biopsy,and optimizing clinical puncture strategies.Methods Retrospective analysis of clinical data of patients undergoing transrectal or perineal prostate biopsy,including age,height,weight,prostate-specific antigen(PSA),prostate volume(PV),PI-RADS v2.1 score,and pathological results of biopsy.The univariate and multivariate Logistic analyses were used to screen out indicators affecting CsPCa,and a prediction model of PSAD combined with PI-RADS v2.1 scores for different PSA intervals was established,and the diagnostic efficacy of the model for CsPCa was evaluated by plotting the receiver operating characteristic(ROC)curves and estimating their area under the curve(AUC)values.Results A total of 282 patients were included according to the inclusion and exclusion criteria.It was found that PSAD and PI-RADS v2.1 score were independent risk fators of CsPCa(P<0.01).The combination model of PSAD and PI-RADS v2.1 score indicated negative predictive value of 96.1%,when PSA<10 ng/ml;when PSA≥10 ng/ml,the ROC curves for PSA,PI-RADS v2.1 score,PI-RADS v2.1 score+PSA,and PI-RADS v2.1 score+PSAD as variables were plotted,with AUC values of 0.711,0.812,0.828,and 0.862 respectively,suggesting that PI-RADS v2.1 score+PASD was significantly better than single PSA(Z=4.047,P=0.000<0.05),single PI-RADS v2.1 score(Z=3.275,P=0.001<0.05),and PI-RADS v2.1 score+PSA(Z=2.658,P=0.008<0.05).Conclusion The predictive model combining PSAD and PI-RADS v2.1 score showed excellent performance to detect CsPCa.It can avoid over-diagnosis and treatment of some patients,thus reducing unnecessary biopsy,especially when PSA<10ng/ml.

关 键 词：前列腺特异性抗原密度 前列腺影像报告及数据分析系统2.1版评分 前列腺特异性抗原 临床有意义前列腺癌 

分 类 号：R737.25[医药卫生—肿瘤]