Department of Radiology, Ewha Womans University College of Medicine, Seoul, South Korea.
Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.
Eur Radiol. 2019 Aug;29(8):4068-4076. doi: 10.1007/s00330-018-5830-3. Epub 2018 Nov 15.
Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps, based on the entire tumor volume, in the differentiation of grades and histological subtypes of meningiomas.
One hundred thirty-six patients with pathologically diagnosed meningiomas (108 low-grade [benign], 28 high-grade [atypical and anaplastic]), who underwent T1C and diffusion tensor imaging, were included in the discovery set. The T1C image, ADC, and FA maps were analyzed to derive volume-based data of the entire tumor. Radiomics features were correlated with meningioma grades and histological subtypes. Various machine learning classifiers were trained to build classification models to predict meningioma grades. We tested the model in a validation set (58 patients; 46 low-grade; 12 high-grade).
The machine learning classifiers showed variable performances depending on the machine learning algorithms. The best classification system for the prediction of meningioma grades had an area under the curve of 0.86 (95% confidence interval [CI], 0.74-0.98) in the validation set. The accuracy, sensitivity, and specificity of the best classifier were 89.7, 75.0, and 93.5% in the validation set, respectively. Various texture parameters differed significantly between fibroblastic and non-fibroblastic subtypes.
Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades.
• Preoperative, noninvasive differentiation of the meningioma grade is important because it influences the treatment strategy. • Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades. • In benign meningiomas, there were significant differences in the various texture parameters between fibroblastic and non-fibroblastic meningioma subtypes.
术前对脑膜瘤的分级进行非侵入性预测很重要，因为它会影响治疗策略。本研究旨在评估基于整个肿瘤体积的对比后 T1 加权图像（T1C）、表观扩散系数（ADC）和各向异性分数（FA）图的放射组学特征在脑膜瘤分级和组织学亚型鉴别中的作用。
纳入了 136 名经病理诊断为脑膜瘤（108 例低级别[良性]，28 例高级别[非典型和间变性]）的患者，这些患者均接受了 T1C 和弥散张量成像检查。对 T1C 图像、ADC 和 FA 图进行分析，以获得整个肿瘤的基于体积的数据。对放射组学特征与脑膜瘤分级和组织学亚型进行相关性分析。采用多种机器学习分类器构建分类模型，以预测脑膜瘤的分级。我们在验证集（58 例患者；46 例低级别；12 例高级别）中对模型进行了测试。
基于机器学习算法的分类器的性能存在差异。在验证集中，用于预测脑膜瘤分级的最佳分类系统的曲线下面积为 0.86（95%置信区间[CI]，0.74-0.98）。最佳分类器在验证集中的准确性、敏感性和特异性分别为 89.7%、75.0%和 93.5%。纤维型和非纤维型亚型之间的各种纹理参数存在显著差异。
T1C 图像、ADC 和 FA 图的放射组学特征为基于机器学习的分类器有助于鉴别脑膜瘤的分级。
• 术前对脑膜瘤的分级进行非侵入性鉴别很重要，因为它会影响治疗策略。• T1C 图像、ADC 和 FA 图的基于放射组学特征的机器学习分类器有助于鉴别脑膜瘤的分级。• 在良性脑膜瘤中，纤维型和非纤维型脑膜瘤亚型之间存在显著的纹理参数差异。
