Hrubá Terézia, Kubas Viliam, Franko Martin, Baláž Vladimír, Spurný Martin, Mištinová Jana Poláková
Radiology Department, F.D. Roosevelt University Hospital, Banská Bystrica, Slovakia.
Urology Clinic, Roosevelt University Hospital, Banská Bystrica, Slovakia.
Ir J Med Sci. 2024 Dec;193(6):2635-2642. doi: 10.1007/s11845-024-03771-w. Epub 2024 Aug 2.
This study focuses on integrating prostate-specific antigen density (PSAD) and Prostate Imaging Reporting and Data System (PI-RADS) for enhanced risk stratification in biopsy-naïve patients.
A prospective study was conducted on 339 patients with suspected prostate cancer, utilizing PSAD and PI-RADS in combination. Logistic regression models were employed, and receiver operating characteristic (ROC) analysis performed to evaluate predictive performance. The patient cohort underwent multiparametric MRI, targeted biopsy, and systematic biopsy.
When patients were stratified into four PSAD risk groups, the rate of clinically significant prostate cancer (csPCa) increased significantly with higher PSAD levels. Logistic regression confirmed the independent contribution of PI-RADS and PSAD, highlighting their role in the prediction of csPCa. Combined models showed superior performance, as evidenced by the area under the curve (AUC) for PI-RADS category and PSAD (0.756), which exceeded that of the individual predictors (PSA AUC, 0.627, PI-RADS AUC 0.689, PSAD AUC 0.708).
This study concludes that combining PSAD and PI-RADS improves diagnostic accuracy and predictive value for csPCa in biopsy-naïve men, resulting in a promising strategy to provide additional risk stratification for more accurate diagnostic decision in biopsy-naïve patients, especially in the PI-RADS 3 group.
本研究聚焦于整合前列腺特异性抗原密度(PSAD)和前列腺影像报告和数据系统(PI-RADS),以增强未接受活检患者的风险分层。
对339例疑似前列腺癌患者进行了一项前瞻性研究,联合使用PSAD和PI-RADS。采用逻辑回归模型,并进行受试者操作特征(ROC)分析以评估预测性能。该患者队列接受了多参数MRI、靶向活检和系统活检。
当患者被分为四个PSAD风险组时,临床显著前列腺癌(csPCa)的发生率随着PSAD水平升高而显著增加。逻辑回归证实了PI-RADS和PSAD的独立贡献,突出了它们在预测csPCa中的作用。联合模型显示出更好的性能,PI-RADS类别和PSAD的曲线下面积(AUC)为0.756,超过了单个预测指标(PSA的AUC为0.627,PI-RADS的AUC为0.689,PSAD的AUC为0.708)。
本研究得出结论,联合使用PSAD和PI-RADS可提高未接受活检男性中csPCa的诊断准确性和预测价值,为未接受活检的患者,尤其是PI-RADS 3组患者提供额外风险分层以做出更准确诊断决策,是一种有前景的策略。