Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jie-Fang Road, Zhejiang, 310009, Hangzhou, China.
Department of Radiology, Yangming Affiliated Hospital, School of Medicine, Ningbo University, Yuyao, 315400, Zhejiang, China.
Cancer Imaging. 2019 May 23;19(1):26. doi: 10.1186/s40644-019-0208-6.
To establish a new accumulating model to enhance the accuracy of prostate cancer (PCa) diagnosis by incorporating prostate-specific antigen (PSA) and its derivative data into the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2).
A total of 357 patients who underwent prostate biopsy between January 2014 and December 2017 were included in this study. All patients had 3.0 T multiparametric magnetic resonance imaging (MRI) and complete laboratory examinations. PI-RADS v2 was used to assess the imaging. PSA, PSA density (PSAD), the free/total PSA ratio (f/t PSA) and the Gleason score (GS) were classified into four-tiered levels, and optimal weights were pursued on these managed levels to build a PCa accumulating model. A receiver operating characteristic curve was generated.
In all, 174 patients (48.7%) had benign prostatic hyperplasia, and 183 (51.3%) had PCa, among whom 149 (81.4%, 149/183) had clinically significant PCa. The established model 6 (PI-RADS v2 + level of PSAD + level of f/t PSA+ level of PSA) had a sensitivity and specificity of 81.4 and 84.5%, respectively, at the cut-off point of 11 in PCa diagnosis. Correspondingly, at the 12 cut-off point, the sensitivity and specificity were 87.7 and 83.0%, respectively, in diagnosing clinically significant PCa. The score of the new accumulating system was significantly different among the defined GS groups (p < 0.001). The mean values and 95% confidence intervals for GS 1-4 groups were 10.20 (9.63-10.40), 12.03 (11.19-12.87), 14.12 (13.60-14.64) and 15.44 (15.09-15.79).
A new PCa accumulating model may be useful in improving the accuracy of the primary diagnosis of PCa and helpful in the clinical decision to perform a biopsy when MRI results are negative.
通过将前列腺特异性抗原（PSA）及其衍生物数据纳入前列腺影像报告和数据系统第 2 版（PI-RADS v2），建立一种新的累积模型，以提高前列腺癌（PCa）诊断的准确性。
本研究共纳入 2014 年 1 月至 2017 年 12 月期间接受前列腺活检的 357 例患者。所有患者均行 3.0T 多参数磁共振成像（MRI）和完整的实验室检查。采用 PI-RADS v2 评估影像学表现。将 PSA、PSA 密度（PSAD）、游离/总 PSA 比值（f/t PSA）和 Gleason 评分（GS）分为四级，并在这些管理水平上寻求最佳权重以建立 PCa 累积模型。生成受试者工作特征曲线。
共有 174 例（48.7%）患者为良性前列腺增生，183 例（51.3%）为 PCa，其中 149 例（81.4%，149/183）为临床显著 PCa。建立的模型 6（PI-RADS v2+PSAD 水平+f/t PSA 水平+PSA 水平）在 PCa 诊断的截断点为 11 时，灵敏度和特异性分别为 81.4%和 84.5%。相应地，在 12 个截断点时，诊断临床显著 PCa 的灵敏度和特异性分别为 87.7%和 83.0%。新累积系统的评分在定义的 GS 组之间存在显著差异（p<0.001）。GS 1-4 组的平均值和 95%置信区间分别为 10.20（9.63-10.40）、12.03（11.19-12.87）、14.12（13.60-14.64）和 15.44（15.09-15.79）。
一种新的 PCa 累积模型可能有助于提高 PCa 初步诊断的准确性，并有助于在 MRI 结果阴性时做出进行活检的临床决策。
