摘要
目的探讨活检前血清炎症标志物对前列腺活检阳性结果的预测价值,建立基于活检前炎症指标联合其他参数的列线图模型,并评价其对前列腺穿刺结果的预测能力。方法法回顾性分析天津医科大学第二医院2019年8月至2021年8月收治的601例行经会阴前列腺穿刺活检患者的临床资料。中位年龄68(35,89)岁。中位tPSA9.56(4.01,19.95)ng/ml。中位fPSA1.36(0.88,2.02)ng/ml。中位PSAD0.16(0.11,0.26)ng/ml^(2)。中位血小板淋巴细胞比值(PLR)129.90(98.95169.89)。PI-RADSv2.1评分<3分189例(31.45%)3分174例(28.95%),4分190例(31.61%),5分48例(7.99%)。采用简单随机分组法将患者分为建模组421例(70%),验证组180例(30%)。两组间的临床资料比较差异均无统计学意义(P>0.05)。对建模组进行单因素和多因素logistic回归分析,筛选预测前列腺活检阳性结果的独立影响因素,建立列线图模型并进行内部验证。在验证组中对该模型进行外部验证。采用受试者工作特征(ROC)曲线验证模型的区分度。采用Hosmer-Lemeshow拟合优度检验验证模型的校准度。采用临床决策曲线(DCA)评价预测模型的净获益和临床效用。结果单因素分析结果显示,建模组中前列腺癌患者与非前列腺癌患者的年龄(OR=1.060,P<0.01)、组织学炎症(OR=0.312,P<0.01)、穿刺针数(OR=0.949,P=0.009)、f/tPSA(OR=0.954,P=0.003)、前列腺体积(OR=0.973,P<0.01)、PSAD(OR=29.260,P<0.01)、PIRADSv2.1评分(3分OR=3.766,P=0.001;4分0R=11.800,P<0.01;5分0R=57.033,P<0.01)、淋巴细胞计数(OR=1.535,P=0.013)、中性粒细胞与淋巴细胞比值(OR=0.848,P=0.044)、PLR(0R=0.994,P=0.005)和系统免疫炎症指数(OR=0.999,P=0.009)差异均有统计学意义。多因素分析结果显示,建模组的年龄(OR=1.094,P<0.01)、fPSA(0R=0.605,P=0.002)、组织学炎症(OR=0.241,P<0.01)、PSAD(OR=7.57,P=0.013)、PLR(0R=0.994,P=0.005)和PI-RADS v2.1评分(3分0R=2.737,P=0.0164分OR=8.621,P<0.01;5分OR=47.65,P<0.01)是初次活检诊断前�
Objective To explore the predictive value of pre-biopsy serum inflammatory markers on positive prostate biopsy results,establish a nomogram model based on pre-biopsy inflammatory markers combined with other parameters,and evaluate its predictive ability for prostate biopsy results.MethodssThe clinical data of 601 patients undergoing transperineal prostate biopsy who were admitted to the Second Hospital of Tianjin Medical University from August 2019 to August 2021 were retrospectively analyzed.The median age was 68(35,89)years,and the median tPSA was 9.56(4.01,19.95)ng/ml.The median fPSA was 1.36(0.88,2.02)ng/ml,the median PSAD was 0.16(0.11,0.26)ng/ml^(2),and the median platelet-to-lymphocyte ratio(PLR)was 129.90(98.95,169.89).PI-RADS v2.1 score<3 points in 189 cases(31.45%),3 points in 174 cases(28.95%),4 points in 190 cases(31.61%),and 5 points in 48 cases(7.99%).A simple randomization method was used to obtain 421 cases(70.00%)in the modeling group and 180 cases(30%)in the validation group.There was no significant difference in the clinical data between the two groups(P>0.05).Univariate and multivariate logistic regression analysis were performed in the modeling group to screen independent influencing factors for the prediction of positive prostate biopsy results.A nomogram model was established and internal verification was conducted.External validation of the model was performed in the validation group.Receiver operating characteristic(ROC)curve was used to verify model discrimination,Hosmer-Lemeshow goodness-of-fit test was used to verify model calibration,and decision curve analysis(DCA)was used to evaluate the net benefit and clinical utility of the predictive model.Results The results of univariate analysis showed that the age(OR=1.060,P<0.01),histological inflammation(OR=0.312,P<0.01),the number of biopsy needles(OR=0.949,P=0.009),f/tPSA(OR=0.954,P=0.003),PV(OR=0.973,P<0.01),PSAD(OR=29.260,P<0.01),PIRADS v2.1 score(3-point OR=3.766,P=0.001;4-point OR=11.800,P<0.01;5-point OR=57.033,P<0.01),lymphocyte co
作者
郭明宇
张宝岭
吴尚融
张洋
陈铭哲
肖雄
姜行康
张洪团
徐勇
刘冉录
Guo Mingyu;Zhang Baoling;Wu Shangrong;Zhang Yang;Chen Mingzhe;Xiao Xiong;Jiang Xingkang;Zhang Hongtuan;Xu Yong;Liu Ranlu(The Second Hospital of Tianjin Medical University,Tianjin Institute of Urology,Tianjin 300200,China)
出处
《中华泌尿外科杂志》
CAS
CSCD
北大核心
2023年第10期752-760,共9页
Chinese Journal of Urology
关键词
前列腺肿瘤
癌
统计学模型
诊断
Prostatic neoplasms
Carcinoma
Models statistical
Diagnosis
