摘要
目的 分析前列腺特异性抗原(PSA)处于灰区(4～10 μg/L)前列腺癌(PCa)发生的影响因素.方法 收集60例前列腺疾病患者的病历资料,根据病理结果将患者分为良性前列腺增生(BPH)组和PCa组,根据格里森评分(GS)将PCa患者分为无临床意义前列腺癌(ncsPCa)组和有临床意义前列腺癌(csPCa)组.采用Logistic回归分析法分析PCa和csPCa发生的影响因素.结果 单因素分析结果显示,BPH组(n=20)和PCa组(n=40)患者总PSA(t-PSA)、PSA密度(PSAD)、多参数MRI(mpMRI)比较,差异均有统计学意义(P﹤0.05);ncsPCa组(n=10,GS﹤7分)和csPCa组(n=30,GS≥7分)患者PSAD、mpMRI比较,差异均有统计学意义(P﹤0.05).多因素Lo-gistic回归分析结果显示,t-PSA高、PSAD高和mpMRI阳性均是PCa发生的独立危险因素(P﹤0.05),PSAD高和mpMRI阳性均是csPCa发生的独立危险因素(P﹤0.05).结论 t-PSA高、PSAD高和mpMRI阳性均是PCa发生的独立危险因素,PSAD高和mpMRI阳性均是csPCa发生的独立危险因素.因此,加强对t-PSA高、PSAD高和mpMRI阳性患者的重视,有助于临床更好地诊断PCa,并且明确区分csPCa和ncsPCa.
Abstract
Objective To investigate the influencing factors of prostate cancer(PCa)with prostate specific antigen(PSA)in the grey area(4～10 μg/L).Method The medical records of 60 patients with prostatic diseases were collected.All patients were divided into benign prostatic hyperplasia(BPH)group and PCa group according to pathological results.The PCa patients were divided into non-clinically significant PCa(ncsPCa)group and clinically significant PCa(csPCa)group according to Gleason score(GS).Logistic regression analysis was used to analyze the influencing factors of PCa and csPCa.Result The results of univariate analysis showed that there were significant differences in total PSA(t-PSA),PSA density(PSAD)and multiparametric MRI(mpMRI)between BPH group(n=20)and PCa group(n=40)(P<0.05),there were significant differences in PSAD and mpMRI between ncsPCa group(n=10)(GS<7 points)and csPCa group(n=30)(GS≥7 points)(P<0.05).Multivariate Logistic regression analysis showed that high t-PSA,high PSAD and mpM-RI positive were independent risk factors for PCa(P<0.05),high PSAD and mpMRI positive were independent risk fac-tors for csPCa(P<0.05).Conclusion High t-PSA,high PSAD and mpMRI positive are independent risk factors for PCa.High PSAD and mpMRI positive are independent risk factors for csPCa.Therefore,paying more attention to patients with high t-PSA,high PSAD and mpMRI positive is helpful to diagnose PCa better and distinguish csPCA from ncsPCA.
NETL
NSTL
万方数据