  Complete resection of malignant gliomas is hampered by the difficulty in
distinguishing tumor cells at the infiltration zone. Fluorescence guidance with
5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work
characterized five fluorophores' emission spectra in most human brain tumors.
In this paper, the effectiveness of these five spectra was explored for
different tumor and tissue classification tasks in 184 patients (891
hyperspectral measurements) harboring low- (n=30) and high-grade gliomas
(n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2),
miscellaneous (n=10) and metastases (n=8). Four machine learning models were
trained to classify tumor type, grade, glioma margins and IDH mutation. Using
random forests and multi-layer perceptrons, the classifiers achieved average
test accuracies of 84-87%, 96%, 86%, and 93% respectively. All five fluorophore
abundances varied between tumor margin types and tumor grades (p < 0.01). For
tissue type, at least four of the five fluorophore abundances were found to be
significantly different (p < 0.01) between all classes. These results
demonstrate the fluorophores' differing abundances in different tissue classes,
as well as the value of the five fluorophores as potential optical biomarkers,
opening new opportunities for intraoperative classification systems in
fluorescence-guided neurosurgery.
