Department of Radiology & Biomedical Imaging, University of California San Francisco, 505 Parnasssus Ave. M-391, San Francisco, CA, 94143, USA.
RadNet Medical Imaging, Walnut Creek, CA, USA.
Abdom Radiol (NY). 2017 Nov;42(11):2725-2731. doi: 10.1007/s00261-017-1169-5.
To evaluate the utility of PI-RADS v2 to diagnose clinically significant prostate cancer (CS-PCa) with magnetic resonance ultrasound (MR/US) fusion-guided prostate biopsies in the non-academic setting.
MATERIALS/METHODS: Retrospective analysis of men whom underwent prostate multiparametric MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. Prostate MRIs were performed on a 3-Tesla scanner with a surface body coil. The Prostate Imaging Reporting and Data System (PI-RADS) v2 scoring algorithm was utilized and MR/US fusion biopsies were performed in selected cases. Mixed effect logistic regression analyses and receiver-operating characteristic (ROC) curves were performed on PI-RADS v2 alone and combined with PSA density (PSAD) to predict CS-PCa.
170 patients underwent prostate MRI with 282 PI-RADS lesions. MR/US fusion diagnosed 71 CS-PCa, 33 Gleason score 3+3, and 168 negative. PI-RADS v2 score is a statistically significant predictor of CS-PCa (P < 0.001). For each one-point increase in the overall PI-RADS v2 score, the odds of having CS-PCa increases by 4.2 (95% CI 2.2-8.3). The area under the ROC curve for PI-RADS v2 is 0.69 (95% CI 0.63-0.76) and for PI-RADS v2 + PSAD is 0.76 (95% CI 0.69-0.82), statistically higher than PI-RADS v2 alone (P < 0.001). The rate of CS-PCa was about twice higher in men with high PSAD (≥0.15) compared to men with low PSAD (<0.15) when a PI-RADS 4 or 5 lesion was detected (P = 0.005).
PI-RADS v2 is a strong predictor of CS-PCa in the non-academic setting and can be further strengthened when utilized with PSA density.
评估 PI-RADS v2 在非学术环境下，通过磁共振超声（MR/US）融合引导的前列腺活检，对临床上显著前列腺癌（CS-PCa）的诊断作用。
材料/方法：对 2014 年 11 月至 2016 年 3 月在单一非学术中心接受前列腺多参数 MRI 检查和随后的 MR/US 融合活检的男性进行回顾性分析。前列腺 MRI 在 3.0T 扫描仪上使用体表线圈进行。采用前列腺成像报告和数据系统（PI-RADS）v2 评分算法，在选定病例中进行 MR/US 融合活检。对 PI-RADS v2 单独以及与 PSA 密度（PSAD）联合预测 CS-PCa 进行混合效应逻辑回归分析和受试者工作特征（ROC）曲线分析。
170 名患者行前列腺 MRI 检查，共 282 个 PI-RADS 病灶。MR/US 融合诊断出 71 例 CS-PCa，其中 33 例为 Gleason 评分 3+3，168 例为阴性。PI-RADS v2 评分是 CS-PCa 的统计学显著预测因子（P<0.001）。PI-RADS v2 总分每增加 1 分，CS-PCa 的发生几率增加 4.2 倍（95% CI 2.2-8.3）。PI-RADS v2 的 ROC 曲线下面积为 0.69（95% CI 0.63-0.76），PI-RADS v2+PSAD 的面积为 0.76（95% CI 0.69-0.82），明显高于 PI-RADS v2 单独（P<0.001）。当检测到 PI-RADS 4 或 5 级病变时，高 PSAD（≥0.15）男性的 CS-PCa 发生率约为低 PSAD（<0.15）男性的两倍（P=0.005）。
PI-RADS v2 在非学术环境下是 CS-PCa 的强有力预测因子，与 PSA 密度联合使用可进一步增强其预测能力。
