Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI
Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI
复制标题
使用多参数 MRI 纹理分析区分良性和非良性脑膜瘤
DOI：
10.1002/jmri.26976
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发表时间：
2019-11-11
影响因子：
4.4
通讯作者：
Feng, Yanqiu
中科院分区：
文献类型：
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作者：
Ke, Chao;Chen, Haolin;Feng, Yanqiu
背景：很难前瞻性地区分良性和恶性肿瘤。（世界卫生组织[WHO] I）和非良性（WHO II和III）脑膜瘤。目的通过使用多参数MR数据的纹理分析，评估术前区分良性和非良性脑膜瘤的可行性。研究类型回顾性。184例脑膜瘤患者（139例良性，45例非良性）作为训练队列，79例脑膜瘤患者（60例良性和19例非良性）被纳入外部验证队列。场强/序列T（1）加权，T-2加权，评价由有经验的放射科医师进行肿瘤分割和影像学特征（RC）评价。首先从预处理后的图像中提取纹理特征，然后将其与RC相结合，并采用两步特征选择的方法对组合特征进行约简。三个单序列模型和一个多参数MRI结果在4种纹理模型中，所有模型均具有较好的预测效果，并通过外部验证队列（External validation cohort）对模型进行评价，计算模型的AUC、Acc、F1、Sen和Spec，以评价模型的预测效果。在训练和外部验证队列中，多参数MRI模型显示出区分良性和非良性脑膜瘤的最佳性能（训练组群中AUC 0.91，Acc 89%，F1 0.88，Sen 0.93，Spec 0.87; AUC 0.83，Acc 80%，F1 0.77，Sen 0.84，和验证队列中的质量标准0.78）结论多参数MR图像纹理分析可用于脑膜瘤的术前鉴别诊断。证据等级：3技术有效性阶段：2 J. Magn. Reson。影像2019.
BackgroundIt is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas.PurposeTo evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data.Study TypeRetrospective.SubjectsIn all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort.Field Strength/SequenceT(1)-weighted, T-2-weighted, and contrast-enhanced T-1-weighted imaging were performed on 1.5 or 3.0T MR systems from two centers.AssessmentTumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort.Statistical TestsArea under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models.ResultsAmong the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort).Data ConclusionNonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019.