Revealing the metabolic profile of brain tumors for diagnosis purposes.
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.
Kounelakis
M G
MG
Technical University of Crete, Department of Electronic and Computer Engineering.
Zervakis
M E
ME
Postma
G J
GJ
Buydens
L M C
LM
Heerschap
A
A
Kotsiakis
X
X
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001921:Brain / Q000378:metabolism*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D011522:Protons; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D011522:Protons