Chryssou Evangelia G, Manikis Georgios C, Ioannidis Georgios S, Chaniotis Vrettos, Vrekoussis Thomas, Maris Thomas G, Marias Kostas, Karantanas Apostolos H
Department of Medical Imaging, University Hospital of Crete, 715 00 Heraklion, Greece.
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 700 13 Heraklion, Greece.
Diagnostics (Basel). 2022 Mar 12;12(3):692. doi: 10.3390/diagnostics12030692.
The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted -value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10 mm/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors.
本研究的目的是探讨基于体素内不相干运动(IVIM)相关直方图分析参数预测子宫内膜癌患者组织学分级的可能性。这项前瞻性研究纳入了52例在我院接受磁共振成像作为初始分期的子宫内膜癌(EC)女性患者,根据病理报告分为低级别(G1和G2)和高级别(G3)肿瘤。在扩散加权图像上绘制感兴趣区(ROI),并使用扩散模型计算表观扩散系数(ADC)、真实扩散率(D)和灌注分数(f)。从全肿瘤直方图计算均值、中位数、偏度、峰度和四分位数间距(IQR)。低级别肿瘤的扩散系数(D)的IQR显著低于高级别组,校正后P值小于5%(0.048)。D的具有统计学意义的IQR的ROC曲线分析结果显示,区分低级别和高级别肿瘤的准确性、敏感性和特异性分别为74.5%、70.1%和76.5%,最佳截断值为0.206(×10⁻³mm²/s),曲线下面积(AUC)为75.4%(95%CI:62.1至88.8)。IVIM建模与直方图分析技术相结合,在术前区分低级别和高级别EC肿瘤方面具有前景。