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雷李智,许乙凯,侯美蓉,何梦琪.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[J].中国医学影像技术,2017,33(7):1047~1051

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

Evaluation of transition zone prostate cancer by Logistic regression of prostate imaging reporting and data system version 2 combined with prostate specific antigen

投稿时间：2016-11-23 修订日期：2017-05-17

DOI：10.13929/j.1003-3289.201611121

中文关键词: 前列腺影像报告和数据系统第2版 Logistic回归模型前列腺肿瘤前列腺特异性抗原

英文关键词:Prostate imaging reporting and data system version 2 Logistic regression models Prostatic neoplasms Prostate-specific antigen

基金项目:

作者	单位	E-mail
雷李智	南方医科大学南方医院影像中心, 广东广州 510515
许乙凯	南方医科大学南方医院影像中心, 广东广州 510515	yikaivip@163.com
侯美蓉	南方医科大学南方医院影像中心, 广东广州 510515
何梦琪	南方医科大学南方医院影像中心, 广东广州 510515

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中文摘要:

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（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+1.507 PI-RADS v2+0.234 t-PSA（χ²=65.993，P＜0.01）；Logit（P）=－5.425+1.906 PI-RADS v2－13.921 f-PSA/t-PSA（χ²=65.993，P＜0.01）；Logit（P）=－7.534+1.045 PI-RADS v2+13.318 PSAD（χ²=74.036，P＜0.01）。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。

英文摘要:

Objective To establish the Logistic regression model by reporting and data system version 2 (PI-RADS v2) and prostate specific antigen (PSA), and to evaluate the diagnostic efficiency in transition zone prostate cancer (PCa). Methods MRI and PSA data of 33 patients with PCa and 54 patients with non-PCa confirmed by pathology were analyzed retrospectively. The PI-RADS v2 was used to evaluate the risk of 2 groups (from low to high as 1 to 5 points). Total PSA(t-PSA), free to total PSA ratio (f-PSA/t-PSA), PSA density (PSAD) and PI-RADS v2 scores were compared between 2 groups. The Logistic regression models were established with parameters which were significantly different between 2 groups. The Logistic regression was divide into three protocols: PI-RADS v2+t-PSA (A), PI-RADS v2+f-PSA/t-PSA (B), PI-RADS v2+PSAD (C). The ROC curves were constructed by the new parameters Logit (P) and PI-RADS v2 scores for assessing the diagnostic efficiency. Results The t-PSA, f-PSA/t-PSA, PSAD and PI-RADS v2 scores had significant differences between the 2 groups (all P＜0.01). Predictive multivariate model of A, B, C was established as Logit(P)=－8.682+1.507 PI-RADS v2+0.234 t-PSA (χ²=65.993, P＜0.01), Logit(P)=－5.425+1.906 PI-RADS v2－13.921 f-PSA/t-PSA (χ²=65.993, P＜0.01), Logit(P)=－7.534+1.045 PI-RADS v2+13.318 PSAD (χ²=74.036, P＜0.01), their area underthe curve (0.945, 0.919, 0.960) were all higher than that of PI-RADS v2 score (0.861, all P＜0.01). The protocol C had the best diagnostic efficiency, and the sensitivity and specificity were 87.88% and 92.59%. The sensitivity and specificity of PI-RADS v2 score were 87.88% and 77.78%. Conclusion The diagnostic efficiency of the Logistic regression model which includes the PI-RADS v2 score and PSA are superior to the PI-RADS v2 score alone for transition zone PCa, which can provide a reliable basis for patients whether need biopsy or not.

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