title: Classification of malignant gliomas by infrared spectroscopic imaging and linear discriminant analysis.
abstract: Infrared (IR) spectroscopy provides a sensitive molecular fingerprint for tissue without external markers. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. Infrared imaging spectrometers equipped with multi-channel detectors combine the spectral and spatial information. Tissue areas of 4 x 4 mm(2) can be analyzed within a few minutes in the macroscopic imaging mode. An approach is described to apply this methodology to human astrocytic gliomas, which are graded according to their malignancy from one to four. Multiple IR images of three tissue sections from one patient with a malignant glioma are acquired and assigned to the six classes normal brain tissue, astrocytoma grade II, astrocytoma grade III, glioblastoma multiforme grade IV, hemorrhage, and other tissue by a linear discriminant analysis model which was trained by data from a single-channel detector. Before the model is applied here, the spectra are shown to be virtually identical. The first specimen contained approximately 95% malignant glioma regions, that means astrocytoma grade III or glioblastoma. The smaller percentage of 12-34% malignant glioma in the second specimen is consistent with its location at the tumor periphery. The detection of less than 0.2% malignant glioma in the third specimen points to a location outside the tumor. The results were correlated with the cellularity of the tissue which was obtained from the histopathologic gold standard. Potential applications of IR spectroscopic imaging as a rapid tool to complement established diagnostic methods are discussed.
authors.affiliation: Institute for Analytical Chemistry, Dresden University of Technology, 01062 Dresden, Germany. ckrafft@units.it
authors.forename: Christoph
authors.initials: C
authors.lastname: Krafft
authors.forename: Stephan B
authors.initials: SB
authors.lastname: Sobottka
authors.forename: Kathrin D
authors.initials: KD
authors.lastname: Geiger
authors.forename: Gabriele
authors.initials: G
authors.lastname: Schackert
authors.forename: Reiner
authors.initials: R
authors.lastname: Salzer
mesh_terms: D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D005909:Glioblastoma / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D006801:Humans; D016014:Linear Models; D008853:Microscopy / Q000379:methods; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013055:Spectrophotometry, Infrared / Q000379:methods*