Histogram Analysis Comparison of Monoexponential, Advanced Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors
Histogram Analysis Comparison of Monoexponential, Advanced Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors
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DOI：
10.1002/jmri.27037
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发表时间：
2020-01-10
影响因子：
4.4
通讯作者：
Qiang, Jinwei
中科院分区：
文献类型：
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作者：
He, Mengge;Song, Yang;Qiang, Jinwei
背景术前准确区分交界性和恶性上皮性卵巢肿瘤（BEOTs vs. MEOTs）对于确定适当的手术策略和改善患者术后生活质量至关重要。几种扩散和灌注MRI技术对于区分是有价值的;然而，哪种是最好的仍不清楚。目的比较弥散加权成像（DWI）、弥散峰度成像（DKI）、体素内非相干运动（IVIM）和动态增强MRI（DCE-MRI）的实体瘤体积直方图分析在鉴别BEOT和MEOT中的价值，并探讨IVIM和DCE-MRI灌注参数之间的相关性。研究类型回顾性。人群20例BEOT患者和42例MEOT患者。场强/序列1.5 T/DWI、DKI和IVIM模型拟合13个不同B因子和40个相位DCE-MRI。评估直方图度量来源于表观扩散系数（ADC）、扩散峰度（K）、扩散系数（Dk）、纯扩散系数（D）、假扩散系数（D*）、灌注分数（f）、体积转移常数（K-transs）、速率常数（k（ep））和血管外细胞外体积分数（v（e））。使用Mann-Whitney U检验和接受者工作特征曲线来确定最佳直方图度量和参数。使用多变量逻辑回归分析来确定四种技术中每两种的最佳组合模型。采用斯皮尔曼等级相关分析IVIM与DCE-MRI参数之间的相关性。结果BEOTs的ADC、D、Dk、D* 均显著高于MEOT（P < 0.05）。BEOT组K、K-transs、k（ep）、v（e）显著低于MEOT组（P < 0.05）。Dk的第10百分位数是最可靠的单一指标，曲线下面积（AUC）为0.921。Dk与K-transs组合产生最高AUC 0.950。D与K-transs呈弱负相关（r =-0.320，P = 0.025），与k（ep）呈弱负相关（r =-0.267，P = 0.037）。数据结论Dk的第10百分位数是最有价值的指标，Dk与K-transs结合具有区分BEOT与MEOT的最佳性能。IVIM和DCE-MRI的灌注相关参数之间无明显联系。技术有效性阶段：2 J. Magn. Reson。影像2020。
Background The accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear. Purpose To compare the whole solid-tumor volume histogram analysis of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE-MRI. Study Type Retrospective. Population Twenty patients with BEOTs and 42 patients with MEOTs. Field Strength/Sequence 1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE-MRI. Assessment Histogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (K-trans), rate constant (k(ep)), and extravascular extracellular volume fraction (v(e)). Statistical Tests The Mann-Whitney U-test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE-MRI parameters. Results ADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, K-trans, k(ep), and v(e) were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with K-trans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and K-trans (r = -0.320, P = 0.025) and between D and k(ep) (r = -0.267, P = 0.037). Data Conclusion The 10th percentile of Dk was the most valuable metric and Dk combined with K-trans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion-related parameters derived from IVIM and DCE-MRI. Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020.