前列腺影像学报告和数据系统版本2.1和前列腺特异性抗原密度在预测临床意义前列腺癌中的影响。

0 UROLOGY & NEPHROLOGY
Sehnaz Tezcan, Funda Ulu Ozturk, Ulku Bekar, Erdem Ozturk
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引用次数: 0

摘要

目的:本研究旨在评估多参数磁共振成像对具有临床意义的前列腺癌症的诊断性能,并确定应用前列腺成像报告和数据系统2.1版评分是否可以改善除生化特征外的诊断途径。材料与方法:本研究纳入199例经多参数磁共振成像的临床疑似前列腺癌症患者。进行逻辑回归分析和受试者操作特征曲线,以确定独立预测因素,并比较具有临床意义的前列腺癌症指标的诊断性能。建立了两个模型。在模型1中,评估了前列腺特异性抗原和前列腺特异性抗原密度衍生参数的诊断性能。在模型2中,分析了模型1加前列腺成像报告和数据系统2.1版评分的预测潜力。结果:通过组织病理学分析,64名患者对具有临床意义的前列腺癌症呈阳性(32.1%)。在模型1中,前列腺特异性抗原密度>0.15被标记为恶性肿瘤的最强预测因子。在模型2中,前列腺特异性抗原密度>0.15,前列腺成像报告和数据系统评分≥3,前列腺成像报道和数据系统得分≥4表明与恶性肿瘤的相关性最强。在这些参数中,前列腺成像报告和数据系统评分≥4(P=0.003)是恶性肿瘤最有力的预测指标,其次是前列腺成像报告与数据系统评分≤3(P=0.012)。多变量分析显示,模型2(76.9%)的准确率高于模型1(67.8%),模型1和模型2的前列腺特异性抗原密度分别为0.632、0.741、0.656和0.798。结论:这些结果表明,前列腺成像报告和数据系统2.1版评分和前列腺特异性抗原密度是临床显著前列腺癌症存在的独立预测因素。前列腺特异性抗原密度和前列腺成像报告和数据系统2.1版评分应在活检而非PSA的决策中得到重视。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Impact of Prostate Imaging Reporting and Data System Version 2.1 and Prostate-Specific Antigen Density in the Prediction of Clinically Significant Prostate Cancer.

The Impact of Prostate Imaging Reporting and Data System Version 2.1 and Prostate-Specific Antigen Density in the Prediction of Clinically Significant Prostate Cancer.

The Impact of Prostate Imaging Reporting and Data System Version 2.1 and Prostate-Specific Antigen Density in the Prediction of Clinically Significant Prostate Cancer.

Objective: The aim of this study was to evaluate the diagnostic performance of multiparametric magnetic resonance imaging for clinically significant prostate cancer and to determine whether applying Prostate Imaging Reporting and Data Systems version 2.1 score could improve the diagnostic pathway besides the biochemical characteristics.

Materials and methods: In this study, 199 patients with clinically suspected prostate cancer who underwent multiparametric magnetic resonance imaging were included. Logistic regression analyses and receiver operating characteristic curve were performed to determine independent predictors and to compare diagnostic performance of indicators for clinically significant prostate cancer. Two models were established. In model 1, the diagnostic performance of prostate-specific antigen- and prostatespecific antigen density-derived parameters were evaluated. In model 2, the prediction potential of model 1 plus Prostate Imaging Reporting and Data Systems version 2.1 score was analyzed.

Results: Sixty-four patients were positive for clinically significant prostate cancer by histopathological analysis (32.1%). In model 1, a prostate-specific antigen density >0.15 was labeled as the strongest predictor of malignancy. In model 2, a prostatespecific antigen density >0.15, a Prostate Imaging Reporting and Data Systems score ≥3, and a Prostate Imaging Reporting and Data Systems score ≥4 demonstrated the strongest association with malignancy. Among these parameters, a Prostate Imaging Reporting and Data Systems score ≥4 (P=.003) was found to be the most robust predictor for malignancy, followed by a Prostate Imaging Reporting and Data Systems score ≥3 (P=.012). The multivariate analysis revealed higher accuracy in model 2 (76.9%) than in model 1 (67.8%). The area under curve values with respect to prostatespecific antigen, prostate-specific antigen density, model 1, and model 2 were 0.632, 0.741, 0.656, and 0.798, respectively.

Conclusion: These results indicated that Prostate Imaging Reporting and Data Systems version 2.1 score and prostate-specific antigen density are independent predictors for the presence of clinically significant prostate cancer. Both prostate-specific antigen density and Prostate Imaging Reporting and Data Systems version 2.1 score should be risen to prominence in the decision of biopsy instead of PSA.

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