联合第2版前列腺影像报告与数据系统评分与前列腺特异性抗原的Logistic回归预测模型诊断移行区前列腺癌

发布时间：2018-01-04 22:10

本文关键词：联合第2版前列腺影像报告与数据系统评分与前列腺特异性抗原的Logistic回归预测模型诊断移行区前列腺癌　出处：《中国医学影像技术》2017年07期 　论文类型：期刊论文

【摘要】：目的建立第2版前列腺影像报告和数据系统(PI-RADS v2)评分联合前列腺特异性抗原(PSA)的Logistic回归预测模型,评价其对移行区前列腺癌(PCa)的诊断价值。方法回顾性分析经病理证实的移行区前列腺腺癌(PCa组,n=33)和良性前列腺增生或前列腺炎(非PCa组,n=54)患者的术前MRI及PSA资料。采用PI-RADS v2对2组进行评分(由低至高评为1~5分)。分析2组的PI-RADS v2评分、总PSA(t-PSA)、游离PSA(f-PSA)与t-PSA比值(fPSA/t-PSA)及PSA密度(PSAD)的差异,选择有统计学意义的指标为自变量,以病理结果是否为PCa为因变量,建立3项Logistic回归模型:PI-RADS v2+t-PSA(A);PI-RADS v2+f-PSA/t-PSA(B);PI-RADS v2+PSAD(C)。建立Logistic回归模型产生的Logit(P)和PI-RADS v2评分的ROC曲线,评估其诊断效能。结果 2组t-PSA、f-PSA/t-PSA、PSAD及PI-RADS v2评分差异均有统计学意义(P均0.01)。A、B、C Logistic回归预测模型分别为:Logit(P)=-8.682+1.507PI-RADS v2+0.234t-PSA(χ~2=65.993,P0.01);Logit(P)=-5.425+1.906PI-RADS v2-13.921f-PSA/t-PSA(χ~2=65.993,P0.01);Logit(P)=-7.534+1.045PI-RADS v2+13.318PSAD(χ~2=74.036,P0.01)。以A、B、C模型产生的Logit(P)预测病理结果,其ROC曲线下面积分别为0.945、0.919、0.960,均高于单独使用PI-RADS v2评分(AUC为0.861),差异有统计学意义(P均0.01)。其中C模型诊断效能最佳,其敏感度、特异度分别为87.88%、92.59%。单独使用PI-RADS v2评分的敏感度、特异度分别为87.88%、77.78%。结论联合PI-RADS v2评分和PSA指标的Logistic回归预测模型对移行区PCa的诊断效能优于单独使用PI-RADS v2评分,为可疑移行区PCa患者行穿刺活检提供了可靠的依据。
[Abstract]:Objective to establish the second edition of prostate imaging reporting and data system (PI-RADS V2) was combined with prostate specific antigen (PSA) of the Logistic regression model, the evaluation of prostate cancer (PCa) diagnostic value. Methods a retrospective analysis of pathologically confirmed prostate adenocarcinoma (PCa group, n=33) benign prostatic hyperplasia and prostatitis (or non PCa group, n=54) in patients with preoperative MRI and PSA data. The PI-RADS V2 score of 2 groups (from low to high rated 1~5). Analysis of 2 groups of PI-RADS V2 score, total PSA (t-PSA), free PSA (f-PSA) and t-PSA ratio (fPSA/t-PSA the density of PSA (PSAD)) and the difference was statistically significant to select indicators as independent variables, with the pathological results is PCa as the dependent variable, establish 3 Logistic regression model: PI-RADS v2+t-PSA (A); PI-RADS v2+f-PSA/t-PSA (B); PI-RADS v2+PSAD (C). Logistic regression model was established to produce Lo Git (P) ROC curve and PI-RADS V2 score, to assess its diagnostic performance. The results of 2 groups of t-PSA, f-PSA/t-PSA, PSAD and PI-RADS V2 score differences were statistically significant (P 0.01).A, B, C and Logistic regression model were: Logit (P) =-8.682+1.507PI-RADS v2+ 0.234t-PSA (~2=65.993, P0.01); Logit (P) =-5.425+1.906PI-RADS v2-13.921f-PSA/t-PSA (~2=65.993, P0.01); Logit (P) =-7.534+1.045PI-RADS v2+13.318PSAD (~2=74.036, P0.01). In A, B, C model Logit (P) prediction of pathological results, the area under the curve of ROC 0.945,0.919,0.960 respectively, were higher than that of PI-RADS alone V2 score (AUC = 0.861), the difference was statistically significant (P < 0.01). The diagnostic efficacy of C model, the sensitivity and specificity were 87.88%, 92.59%. used alone PI-RADS V2 score of sensitivity and specificity were 87.88%, 77.78%. conclusion: the combination of PI-RADS and V2 score The Logistic regression prediction model with PSA index is superior to the PI-RADS V2 score in the diagnosis of PCa in the transitional zone, which provides a reliable basis for biopsy in the suspected migrating area PCa patients.

【作者单位】：南方医科大学南方医院影像中心;
【分类号】：R445.2;R737.25
【正文快照】： 组,n=33)和良性前列腺增生或前列腺炎(非PCa组,n=54)患者的术前MRI及PSA资料。采用PI-RADS v2对2组进行评分(由低至高评为1~5分)。分析2组的PI-RADS v2评分、总PSA(t-PSA)、游离PSA(f-PSA)与t-PSA比值(f-PSA/t-PSA)及PSA密度(PSAD)的差异,选择有统计学意义的指标为自变量,以病

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