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Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy

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.
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https://hal.archives-ouvertes.fr/hal-02440653
Contributor : Cédric Ray <>
Submitted on : Wednesday, January 29, 2020 - 5:14:51 PM
Last modification on : Thursday, July 30, 2020 - 9:42:57 AM

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Pierre Leclerc, Cédric Ray, Laurent Mahieu-Williame, Laure Alston, Carole Frindel, et al.. Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Scientific Reports, Nature Publishing Group, 2020, 10 (1), pp.1462 et suiv. ⟨10.1038/s41598-020-58299-7⟩. ⟨hal-02440653v2⟩

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