Kounelakis M G, Zervakis M E, Postma G J, Buydens L M C, Heerschap A, Kotsiakis X
Technical University of Crete, Department of Electronic and Computer Engineering.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:35-8. doi: 10.1109/IEMBS.2009.5334984.
The metabolic behavior of complex brain tumors, like Gliomas and Meningiomas, with respect to their type and grade was investigated in this paper. Towards this direction the smallest set of the most representative metabolic markers for each brain tumor type was identified, using ratios of peak areas of well established metabolites, from (1)H-MRSI (Proton Magnetic Resonance Spectroscopy Imaging) data of 24 patients and 4 healthy volunteers. A feature selection method that embeds Fisher's filter criterion into a wrapper selection scheme was applied; Support Vector Machine (SVM) and Least Squares-SVM (LS-SVM) classifiers were used to evaluate the ratio markers classification significance. The area under the Receiver Operating Characteristic curve (AUROC) was adopted to evaluate the classification significance. It is found that the NAA/CHO, CHO/S, MI/S ratios can be used to discriminate Gliomas and Meningiomas from Healthy tissue with AUROC greater than 0.98. Ratios CHO/S, CRE/S, MI/S, LAC/CRE, ALA/CRE, ALA/S and LIPS/CRE can identify type and grade differences in Gliomas giving AUROC greater than 0.98 apart from the scheme of Gliomas grade II vs grade III where 0.84 was recorded due to high heterogeneity. Finally NAA/CRE, NAA/S, CHO/S, MI/S and ALA/S manage to discriminate Gliomas from Meningiomas providing AUROC exceeding 0.90.
本文研究了复杂脑肿瘤（如胶质瘤和脑膜瘤）在类型和分级方面的代谢行为。朝着这个方向，利用24名患者和4名健康志愿者的（1）H-MRSI（质子磁共振波谱成像）数据中成熟代谢物的峰面积比，确定了每种脑肿瘤类型最具代表性的最小代谢标志物集。应用了一种将Fisher滤波准则嵌入包装选择方案的特征选择方法；使用支持向量机（SVM）和最小二乘支持向量机（LS-SVM）分类器来评估比率标志物的分类意义。采用接收器操作特征曲线下面积（AUROC）来评估分类意义。结果发现，NAA/CHO、CHO/S、MI/S比率可用于将胶质瘤和脑膜瘤与健康组织区分开来，AUROC大于0.98。CHO/S、CRE/S、MI/S、LAC/CRE、ALA/CRE、ALA/S和LIPS/CRE比率可以识别胶质瘤的类型和分级差异，除了二级与三级胶质瘤的方案，由于高度异质性，该方案的AUROC为0.84。最后，NAA/CRE、NAA/S、CHO/S、MI/S和ALA/S成功地将胶质瘤与脑膜瘤区分开来，AUROC超过0.90。
