Oi Yuta, Minamoto Haruki, Taniyama Ichita, Fukuzawa Masayuki, Sakai Koji, Ogawa Takahiro, Yamanaka Takumi, Takahashi Yoshinobu, Hashimoto Naoya
Department of Neurosurgery, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Department of Neurosurgery, National Hospital Organization Maizuru Medical Center, Maizuru, Japan.
J Neurooncol. 2025 Sep 16. doi: 10.1007/s11060-025-05186-8.
Predicting future tumor growth from initial imaging of incidentally discovered meningioma (IDM) could inform treatment decisions. However, most factors identified in prior studies on meningioma growth are qualitative. The aim of this study is to identify factors associated with tumor growth using quantitative radiomics features from MRI data.
MRI T2 features from initial imaging of 24 tumor growth cases were compared with those of 25 cases without growth. An in-house program was developed to reduce the time required for data analysis. This program is based on the open-source software 3D Slicer 5.6.2 and PyRadiomics 3.1.0. It enables semi-automatic batch t-test analyses for each feature to compare tumor growth and non-growth groups. Regions of interest (ROIs) were placed in the tumor, outer tumor edge, whole brain, and white matter contralateral to the tumor. A total of 107 features were analyzed across seven classifications: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighboring Gray Tone Difference Matrix. A t-test was used to identify significant predictors.
Ten features across five classifications showed significant differences (p < 0.05): 2 First Order statistics, 2 Shape features, 4 Gy Level Co-occurrence Matrices, 1 Gy Level Size Zone Matrix, and 1 Neighboring Gray Tone Difference Matrix.
Potential predictors of IDM growth were identified using radiomics features. Future studies with larger cohorts and validation will be essential to confirm the clinical utility and improve the predictive accuracy of these features.
通过偶然发现的脑膜瘤（IDM）的初始影像学检查预测未来肿瘤生长情况，可为治疗决策提供参考。然而，先前关于脑膜瘤生长的研究中确定的大多数因素都是定性的。本研究的目的是利用MRI数据的定量放射组学特征来识别与肿瘤生长相关的因素。
将24例肿瘤生长病例的初始MRI T2特征与25例无肿瘤生长病例的特征进行比较。开发了一个内部程序以减少数据分析所需的时间。该程序基于开源软件3D Slicer 5.6.2和PyRadiomics 3.1.0。它能够对每个特征进行半自动批量t检验分析，以比较肿瘤生长组和非生长组。将感兴趣区域（ROI）放置在肿瘤、肿瘤外边缘、全脑以及肿瘤对侧的白质中。共分析了七个分类中的107个特征：一阶统计量、形状、灰度共生矩阵、灰度游程长度矩阵、灰度大小区域矩阵、灰度依赖矩阵和相邻灰度色调差异矩阵。使用t检验来识别显著的预测因子。
五个分类中的10个特征显示出显著差异（p < 0.05）：2个一阶统计量、2个形状特征、4个灰度共生矩阵、1个灰度大小区域矩阵和1个相邻灰度色调差异矩阵。
利用放射组学特征识别出了IDM生长的潜在预测因子。未来进行更大样本量队列研究和验证对于确认这些特征的临床实用性并提高预测准确性至关重要。
