Niu Lei, Zhou Xiaoming, Duan Chongfeng, Zhao Jiping, Sui Qinglan, Liu Xuejun, Zhang Xuexi
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao.
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao.
World Neurosurg. 2019 Jun;126:e646-e652. doi: 10.1016/j.wneu.2019.02.109. Epub 2019 Mar 1.
Meningioma subtypes are one of the most common key points to the treatment and prognosis of patients. The purpose of this study was to investigate the differential diagnostic value of radiomics features on meningioma.
A total of 241 patients with meningioma who had undergone tumor resection were randomly selected including 80 with meningothelial meningioma, 80 with fibrous meningioma, and 81 with transitional meningioma. These meningiomas were divided into 4 groups including: meningothelial versus fibrous (group 1), fibrous versus transitional (group 2), meningothelial versus transitional (group 3), and meningothelial versus fibrous versus transitional (group 4). All patients were examined using the same magnetic resonance scanner (GE 3.0 T) and the preoperative contrast-enhanced T1-weighted images were available. Radiomics features from the contrast-enhanced T1-weighted images of 241 patients were evaluated by 2 experienced radiology specialists.
A total of 385 radiomics features were extracted from the images of each patient. Several preprocessing methods were applied on the radiomics dataset to reduce the redundancy and highlight differences between different meningioma before the Fisher discrimination analysis was adopted and leave one out cross validation methods were used for the model validation. The differentiation accuracies of the Fisher discriminant analysis model for groups 1, 2, 3, and 4 were 99.4%, 98.8%, 100% and 100%, respectively; leave one out cross validation method was achieved for group 1, 2, 3, and 4 with the accuracies of 91.3%, 95.0%, 100%, and 94.2%, respectively.
Radiomics features and the combined Fisher discriminant analysis could provide satisfactory performance in the preoperative differential diagnosis of meningioma subtypes and enable the potential ability for clinical application.
脑膜瘤亚型是患者治疗和预后的最常见关键点之一。本研究的目的是探讨影像组学特征在脑膜瘤鉴别诊断中的价值。
随机选取241例行肿瘤切除术的脑膜瘤患者，其中脑膜内皮型脑膜瘤80例，纤维型脑膜瘤80例，过渡型脑膜瘤81例。这些脑膜瘤分为4组，包括：脑膜内皮型与纤维型（第1组）、纤维型与过渡型（第2组）、脑膜内皮型与过渡型（第3组）以及脑膜内皮型与纤维型与过渡型（第4组）。所有患者均使用同一台磁共振扫描仪（GE 3.0 T）进行检查，且有术前增强T1加权图像。由2名经验丰富的放射科专家评估241例患者增强T1加权图像的影像组学特征。
从每位患者的图像中提取了总共385个影像组学特征。在采用Fisher判别分析之前，对影像组学数据集应用了几种预处理方法，以减少冗余并突出不同脑膜瘤之间的差异，并使用留一法交叉验证方法进行模型验证。第1、2、3和4组Fisher判别分析模型的鉴别准确率分别为99.4%、98.8%、100%和100%；第1、2、3和4组采用留一法交叉验证方法，准确率分别为91.3%、95.0%、100%和94.2%。
影像组学特征及联合Fisher判别分析在脑膜瘤亚型术前鉴别诊断中可提供满意的表现，并具有临床应用的潜力。
