Mori Naoko, Mugikura Shunji, Endo Toshiki, Endo Hidenori, Oguma Yo, Li Li, Ito Akira, Watanabe Mika, Kanamori Masayuki, Tominaga Teiji, Takase Kei
Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, Japan.
Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan.
Neuroradiology. 2023 Feb;65(2):257-274. doi: 10.1007/s00234-022-03045-1. Epub 2022 Aug 31.
To investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.
Consecutive patients diagnosed with meningioma by surgery (77 low-grade and 28 non-low-grade meningiomas) underwent preoperative magnetic resonance imaging including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI). Manual segmentation of the tumor area was performed to extract texture features. Segmentation of the peritumoral area was performed for peritumoral high-signal intensity (PHSI) on T2WI. Principal component analysis was performed to fuse the texture features to principal components (PCs), and PCs of each sequence of the tumor and peritumoral areas were compared between low- and non-low-grade meningiomas. Only PCs with statistical significance were used for the model construction using a support vector machine algorithm. k-fold cross-validation with receiver operating characteristic curve analysis was used to evaluate diagnostic performance.
Two, one, and three PCs of T1WI, apparent diffusion coefficient (ADC), and CE-T1WI, respectively, for the tumor area, were significantly different between low- and non-low-grade meningiomas, while PCs of T2WI for the tumor area and PCs for the peritumoral area were not. No significant differences were observed in PHSI. Among models of sequence combination, the model with PCs of ADC and CE-T1WI for the tumor area showed the highest area under the curve (0.84).
The model with PCs of ADC and CE-T1WI for the tumor area showed the highest diagnostic performance for differentiating between low- and non-low-grade meningiomas. Neither PHSI nor PCs in the peritumoral area showed added value.
研究基于序列组合的肿瘤及瘤周区域纹理特征能否区分低级别和非低级别脑膜瘤。
连续77例经手术诊断为低级别脑膜瘤及28例非低级别脑膜瘤患者术前行磁共振成像检查，包括T1加权成像（T1WI）、T2加权成像（T2WI）、扩散加权成像（DWI）及对比增强T1WI（CE-T1WI）。对肿瘤区域进行手动分割以提取纹理特征。在T2WI上对瘤周高信号强度（PHSI）区域进行瘤周分割。进行主成分分析将纹理特征融合为主成分（PC），比较低级别和非低级别脑膜瘤肿瘤及瘤周区域各序列的PC。仅将具有统计学意义的PC用于支持向量机算法的模型构建。采用k折交叉验证及受试者操作特征曲线分析评估诊断性能。
低级别和非低级别脑膜瘤肿瘤区域T1WI、表观扩散系数（ADC）及CE-T1WI的PC分别有2个、1个和3个存在显著差异，而肿瘤区域T2WI的PC及瘤周区域的PC无显著差异。PHSI未见显著差异。在序列组合模型中，肿瘤区域ADC和CE-T1WI的PC组成的模型曲线下面积最高（0.84）。
肿瘤区域ADC和CE-T1WI的PC组成的模型在区分低级别和非低级别脑膜瘤方面诊断性能最高。瘤周区域的PHSI及PC均未显示出额外价值。
