A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy.
A new classification approach was developed to improve the noninvasive diagnosis of brain tumors. Within this approach, information is extracted from magnetic resonance imaging and spectroscopy data, from which the relative location and distribution of selected tumor classes in feature space can be calculated. This relative location and distribution is used to select the best information extraction procedure, to identify overlapping tumor classes, and to calculate probabilities of class membership. These probabilities are very important, since they provide information about the reliability of classification and might provide information about the heterogeneity of the tissue. Classification boundaries were calculated by setting thresholds for each investigated tumor class, which enabled the classification of new objects. Results on histopathologically determined tumors are excellent, demonstrated by spatial maps showing a high probability for the correctly identified tumor class and, moreover, low probabilities for other tumor classes.
Simonetti
Arjan W
AW
Laboratory for Analytical Chemistry, University of Nijmegen, Toernooiveld 1 6525 ED Nijmegen, The Netherlands.
Melssen
Willem J
WJ
van der Graaf
Marinette
M
Postma
Geert J
GJ
Heerschap
Arend
A
Buydens
Lutgarde M C
LM
D001224:Aspartic Acid / Q000031:analogs & derivatives* / Q000032:analysis; D001921:Brain / Q000473:pathology; D001923:Brain Chemistry; D001932:Brain Neoplasms / Q000145:classification* / Q000175:diagnosis; D002555:Cerebrospinal Fluid / Q000737:chemistry; D002794:Choline / Q000032:analysis; D003401:Creatine / Q000032:analysis; D016002:Discriminant Analysis; D005227:Fatty Acids / Q000032:analysis; D005910:Glioma / Q000145:classification / Q000175:diagnosis; D018698:Glutamic Acid / Q000032:analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007294:Inositol / Q000032:analysis; D019344:Lactic Acid / Q000032:analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy; D018579:Patient Selection; D025341:Principal Component Analysis; D011336:Probability; D012680:Sensitivity and Specificity; D016008:Statistical Distributions
D005227:Fatty Acids; D001224:Aspartic Acid; D019344:Lactic Acid; D018698:Glutamic Acid; D007294:Inositol; C000179:N-acetylaspartate; D003401:Creatine; D002794:Choline