Abstract
Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarker-based prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue.
Highlights
Gliomas account for more than fifty percent of primitive brain tumors
Pre-operative MRI combined with neuro-navigation is currently used in the operating theater[6,7] but shows strong limitations8–10. 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence microscopy has shown its relevance in neuro-oncology[11]
Instead of choosing a small amount of numerical features used as biomarkers like in the recent above-cited literature[6,15,16,17,18,19,20,21,22,23,24], we propose to investigate the prediction of glioma margin with an entirely data-driven approach where no assumption of feature selection based on expert is made
Summary
Gliomas account for more than fifty percent of primitive brain tumors. They are infiltrative tumors, with a margin difficult to identify and discriminate from the surrounding healthy tissues. This is done on the same data acquired previously in a surgical procedure of 5-ALA induced PpIX guided glioma removal and which had been only processed so far with a biomarker approach[23]. This choice enables a comparison of the prediction performance of glioma margin from a biomarker approach with a novel expert-independent point of view. As another element of novelty, the prediction of glioma margin is performed from 3 different fluorescent spectra in response to 3 distinct excitation wavelengths taken successively over the same area, while previously only a few features extracted from a single fluorescent spectrum were used for analysis[23]
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