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Quantitative dynamic contrast-enhanced MR imaging for differentiating benign, borderline, and malignant ovarian tumors

机译:定量动态对比增强MR成像,用于区分良性,边界和恶性卵巢肿瘤

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Purpose This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. Methods We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n?=?28) and type II (n?=?29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal–Wallis test, Mann–Whitney U test, Pearson’s chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. Results The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P?
机译:目的本研究旨在探讨定量DCE-MRI对表征卵巢肿瘤的诊断性能。方法我们预期评估了15个良性,28个边界和66个恶性卵巢肿瘤中的定量DCE-MRI参数(Ktrans,Kep和Ve)的差异;在I型(n?=Δ28)之间,上皮卵巢癌(EoC)的II型(n?=α29)。使用整体实体肿瘤体积区域(ROI)方法分析DCE-MRI数据,并基于修改的TOFTS模型计算定量参数。非参数kruskal-wallis测试,Mann-Whitney U测试,Pearson的Chi-Square测试,脑内相关系数(ICC),方差测试和接收器操作特征曲线(ROC)用于统计分析。结果在卵巢恶性肿瘤中观察到最大的ktrans和kep值,其次是横向和良性肿瘤(所有p?<0.001)。 KEP是将良性肿瘤与边缘线和恶性肿瘤差异的更好参数,敏感性为89.3%和95.5%,特异性为86.7%和100%,精度为88.4%和96.3%,以及曲线下的一个区域(分别为0.94和0.992的AUC,而ktrans则更好地为分化来自恶性肿瘤的侧边缘,敏感性为60.7%,特异性为78.8%,准确度为73.4%,AUC为0.743。此外,与KEP的组合可以进一步将敏感性提高到78.9%。 II型中位Ktrans和Kep值比I型EoC在I类中显着高。结论具有体积定量的DCE-MRI是一种技术上可行的方法,可用于卵巢肿瘤的分化,并用于区分I型和II型EOC。

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