基于超声弹性成像建立预测前列腺癌风险的列线图模型  

Development of a nomogram for predicting the risk of prostate cancer based on transrectal ultrasound elastography

作　　者：李方龙 杜亚斌 张荣心 杜春花 赵新鸿 邱建宏 LI Fanglong;DU Yabin;ZHANG Rongxin;DU Chunhua;ZHAO Xinhong;QIU Jianhong(The 980th Hospital of the PLA Joint Logistic Support Force,Shijiazhuang 050082,China)

机构地区：[1]解放军联勤保障部队第九八○医院泌尿外科,石家庄050082 [2]解放军联勤保障部队第九八○医院超声诊断科,石家庄050082

出　　处：《现代泌尿生殖肿瘤杂志》2025年第2期101-105,共5页Journal of Contemporary Urologic and Reproductive Oncology

基　　金：河北省医学科学研究课题计划资助(20231341)。

摘　　要：目的探讨超声弹性成像技术在前列腺癌(PCa)诊断中的应用价值。方法收集2021年1月至2024年4月行经直肠超声引导下前列腺穿刺活检患者的临床资料,包括年龄、总体前列腺特异抗原(tPSA)、游离前列腺特异抗原(fPSA)、前列腺体积(PV)、前列腺特异抗原密度(PSAD)、经直肠超声弹性成像评分(UES)、病理结果等,资料完整者纳入研究,共235例。采用电脑随机分组法选择188例(80%)患者为建模组,其余47例(20%)为验证组。在建模组中比较UES在PCa组和前列腺增生(BPH)组之间的差异,并利用多因素Logistic回归筛选预测PCa的独立性预测指标,并构建回归方程,在此基础上建立预测PCa的列线图模型。以受试者工作特征曲线(ROC)评估模型及其他指标对PCa的诊断价值。结果在建模组中,PCa 68例,前列腺增生患者120例。PCa和前列腺增生组中UES分别为(2.88±0.478)分和(2.07±0.634)分,二者存在明显差异(P<0.001);并且tPSA、fPSA、PV、和PSAD在两组间的差异有统计学意义(P<0.05)。多因素Logistic分析表明tPSA、PV和UES是预测PCa的独立性预测指标,利用上述指标构建列线图模型,该模型预测PCa的ROC曲线下面积为0.920,高于UES(0.817),PSAD(0.822),游离/总前列腺特异抗原(0.549),差异有统计学意义(P<0.05)。在验证组中亦得到类似的结果。结论UES可用于PCa的诊断,并且基于UES构建的列线图模型较单独指标具有更高的预测价值,该模型可以为患者以直观简洁形式提供个体化PCa风险预测。Objective To investigate the value of ultrasound elastography in the diagnosis of prostate cancer.Methods We analyzed the clinical data of patients who underwent prostate biopsy for prostate cancer in our hospital from January 2021 to April 2024.Variables including age,total prostate specific antigen(tPSA),free prostate specific antigen(fPSA),prostate volume(PV),prostate specific antigen density(PSAD),transrectal ultrasound elastography score(UES),pathology report were collected.In this study,235 patients with complete data were enrolled.Of these patients,we randomly selected 80%as development group(188),and the other 20%as validation group(47).In the development group,the difference of UES between the prostate cancer group and the prostate hyperplasia group was compared,and multivariate Logistic regression was used to select independent predictors of prostate cancer.A nomogram for predicting prostate cancer was established according to regression equation,and ROC curve was used to evaluate the diagnostic value of the model and other indicators for prostate cancer.Results In development group,the UES of prostate cancer(2.88±0.478)was higher than prostate hyperplasia group(2.07±0.634)with significant difference(P<0.001).And tPSA,fPSA,PV,and PSAD were significantly different between the two groups(P<0.05).Multivaiate logisitic analysis showed that tPSA,PV and UES were independent predictors of prostate cancer.A nomogram model was constructed using the above indicators.The area under ROC curve of this model was 0.920,higher than UES(0.817),PSAD(0.822)and f/tPSA(0.549),and the difference was statistically significant(P<0.05).Similar results were obtained in the validation group.Conclusions UES can be used in the diagnosis of prostate cancer,and the nomogram model constructed based on UES has higher predictive value than the single index,which can provide patients with personalized prostate cancer risk prediction in an intuitive and concise form.

关 键 词：前列腺癌 超声弹性成像 预测模型 

分 类 号：R737.25[医药卫生—肿瘤]