Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.
Department of Radiology, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.
Eur J Radiol. 2022 Jul;152:110288. doi: 10.1016/j.ejrad.2022.110288. Epub 2022 Apr 2.
The aim of the study was to evaluate the feasibility of texture analysis in differentiating between posterior fossa ependymoma type A (PF-EPN-A) and type B (PF-EPN-B) among children.
Our retrospective study included 43 patients (37 PF-EPN-A and 6 PF-EPN-B) who were pathologically diagnosed with ependymomas in the posterior fossa. The texture features were extracted automatically from the volume of interests (VOIs), which were manually delineated on fluid-attenuated inversion recovery (FLAIR), contrast-enhanced T1-weighted (T1C), and diffusion-weighted imaging (DWI) MRI sequences. A receiver operating characteristic curve (ROC) was built to assess the diagnostic value of the texture parameters, and the prognostic value was evaluated by survival analysis.
Texture parameter [Wavelet-LHH (H: High pass filter, L: Low pass. filter)_glcm (gray-level co-occurrence matrix)_Idn (Inverse difference normalized)] provides valuable information in distinguishing subgroups of ependymomas with higher specificity and positive predictive value (PPV). A total of 27 patients were divided into a high-risk group (IDN value＞0.916) and a low-risk group (IDN value＜0.916) with the most optimistic cut-off value (0.916). The Kaplan-Meier analysis of the survival curves showed significantly longer disease-free survival for low-risk groups compared to high-risk groups [hazard ratio (HR): 0.28, 95% confidence interval (CI): 0.11-0.69; p = 0.017].
Our results suggested that the texture parameters based on DWI images can be used to differentiate PF-EPN-A from PF-EPN-B. Texture analysis could be used as a noninvasive tool in distinguishing subgroup pediatric posterior fossa ependymomas and provide reliable prognostic information upon the verification of its reproducibility and feasibility by further studies.
本研究旨在评估纹理分析在区分儿童后颅窝室管膜瘤 A 型（PF-EPN-A）和 B 型（PF-EPN-B）中的可行性。
本回顾性研究纳入了 43 名经病理诊断为后颅窝室管膜瘤的患者（37 名 PF-EPN-A 和 6 名 PF-EPN-B）。纹理特征从手动勾画的感兴趣区（VOI）中自动提取，VOI 分别在液体衰减反转恢复（FLAIR）、对比增强 T1 加权（T1C）和弥散加权成像（DWI）序列上勾画。构建受试者工作特征曲线（ROC）评估纹理参数的诊断价值，通过生存分析评估预后价值。
纹理参数[小波-LHH（H：高通滤波器，L：低通滤波器）_glcm（灰度共生矩阵）_Idn（倒数差归一化）]提供了有价值的信息，可用于区分具有更高特异性和阳性预测值（PPV）的室管膜瘤亚组。共有 27 名患者根据 IDN 值（IDN 值＞0.916 为高风险组，IDN 值＜0.916 为低风险组）分为高风险组和低风险组。基于 IDN 值的生存曲线 Kaplan-Meier 分析显示，低风险组的无疾病生存时间明显长于高风险组[风险比（HR）：0.28，95%置信区间（CI）：0.11-0.69；p=0.017]。
我们的研究结果表明，基于 DWI 图像的纹理参数可用于区分 PF-EPN-A 和 PF-EPN-B。纹理分析可作为一种非侵入性工具，用于区分小儿后颅窝室管膜瘤亚组，并在进一步研究验证其可重复性和可行性后提供可靠的预后信息。
