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首页> 外文期刊>Brain Sciences >Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
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Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

机译:在磁共振的脑转移中区分高级胶质瘤:骨质区纹理分析的作用

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High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
机译:高级胶质瘤(HGGS)和孤零零脑转移(BMS)具有类似的成像外观,这通常导致错误分类。在HGGS中,周围的组织显示恶性侵犯,而BMS倾向于取代相邻区域。由两者产生的周围的水肿不能通过常规磁共振(MRI)检查来区分。回顾包括常规预处理MRI的常规预处理MRI的病理学脑肿瘤(HGGS,N = 16; BMS,N = 26)。使用专用软件对T2加权序列进行Peritumoral区的纹理分析。使用Fisher选择最差异的纹理特征和分类误差和平均相关系数的概率。通过单变量,接收器操作和多变量分析来评估纹理参数以区分Hggs和BMS的能力。第一百分位数和小波能量纹理参数是HGGs的独立预测因子(敏感度75-87.5%,特异性53.85-88.46%)。由在单变量分析中显示出统计上显着结果的所有参数组成的预测模型能够鉴定具有100%敏感性和66.7%的特异性的HGG。纹理分析可以提供孤立脑肿瘤中遇到的Peritumoral区的定量描述,可以在Hggs和BMS之间提供足够的分化。

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