Wen Jing, Tang Tingting, Ji Yugang, Zhang Yilan
Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China.
Department of Radiology, Yancheng First Peoples' Hospital, Yancheng, China.
Front Oncol. 2022 Apr 8;12:861928. doi: 10.3389/fonc.2022.861928. eCollection 2022.
To evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa).
A total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa.
A total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186).
The combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.
评估将前列腺影像报告和数据系统（PI-RADS）v2.1评分系统与前列腺特异性抗原密度（PSAD）相结合检测前列腺癌（PCa）的诊断性能。
共有266名疑似PCa的参与者在我院接受了多参数磁共振成像（mpMRI）检查，至少4周后，所有患者均接受了后续的系统性经直肠超声（TRUS）引导活检或MRI-TRUS融合靶向活检。所有mpMRI图像均按照PI-RADS v2.1进行评分，并进行单因素和多因素逻辑回归分析以确定PCa的显著预测因素。
活检中共有119例患者被诊断为PCa，其中101例患者被诊断为具有临床意义的PCa。多因素分析显示，PI-RADS v2.1和PSAD是PCa的独立预测因素。对于外周带（PZ），PI-RADS评分与PSAD联合的ROC曲线下面积（AUC）为0.90（95%CI 0.83-0.96），显著优于单独使用PI-RADS评分（0.85，95%CI 0.78-0.93，P=0.031）和单独使用PSAD（0.83，95%CI 0.75-0.90，P=0.037）。然而，对于移行带（TZ），联合模型并不显著优于单独使用PI-RADS，AUC分别为0.94（95%CI 0.89-0.99）和0.93（95%CI 0.88-0.97，P=0.186）。
PI-RADS v2.1与PSAD相结合可显著提高PZ中PCa的诊断性能。然而，在TZ中PCa方面未观察到显著改善。
