Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data
Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data
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DOI：
10.1007/s11548-012-0808-0
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发表时间：
2013-09-01
影响因子：
3
通讯作者：
Tsougos, Ioannis
中科院分区：
文献类型：
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作者：
Tsolaki, Evangelia;Svolos, Patricia;Tsougos, Ioannis
目的 区分胶质母细胞瘤和转移瘤具有重要的临床意义，但即使对于专家观察者来说也可能很困难。为了研究机器学习算法在区分多形性胶质母细胞瘤 (GB) 与转移瘤中的贡献，我们开发并测试了基于 3T 磁共振 (MR) 数据的模式识别系统。 材料和方法 对 49 名孤立性脑肿瘤患者（35 名多形性胶质母细胞瘤和 14 名多形性胶质母细胞瘤）进行了单体素和多体素质子磁共振波谱 (1H-MRS) 和动态磁敏感对比 (DSC) MRI 扫描。转移）。测量瘤内和瘤周区域的代谢（NAA/Cr、Cho/Cr、(Lip Lac)/Cr）和灌注（rCBV）参数。评估了这些参数的统计显着性。对于分类过程，创建了三个数据集，以找到提供最大区分度的最佳参数组合。使用了三种机器学习方法：Na < ve-Bayes、支持向量机 (SVM) 和最近邻 (KNN)。每个分类器的辨别能力通过定量性能指标进行评估。结果胶质母细胞瘤和转移瘤仅在这些病变的瘤周区域是可区分的（）。 SVM 在肿瘤内和肿瘤周围区域均取得了最高的总体性能（准确度 98%）。 Na < ve-Bayes 和 KNN 在性能上表现出更大的变化。数据集的正确选择起着非常重要的作用，因为它们与潜在的病理生理学密切相关。 结论 使用基于 3T MR 的灌注和代谢特征的模式识别技术的应用可以为常见的轴内脑肿瘤（例如胶质母细胞瘤与转移瘤）的区分提供增量诊断价值。
Purpose Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data.Materials and Methods Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Na < ve-Bayes, Support Vector Machine (SVM) and -nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics.Results Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions (). SVM achieved the highest overall performance (accuracy 98 %) for both the intratumoral and peritumoral areas. Na < ve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology.Conclusion The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.