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

Pierre Leclerc Cédric Ray 1 Laurent Mahieu-Williame 2 Laure Alston 3 Carole Frindel 4 Pierre-François Brevet David Meyronet 5 Jacques Guyotat 6 Bruno Montcel 3 David Rousseau 7
2 PILoT - Plateforme d'Imagerie Multimodale LyonTech
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
3 RMN et optique : De la mesure au biomarqueur
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
4 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
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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Submitted on : Wednesday, January 15, 2020 - 11:58:52 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, inPress. ⟨hal-02440653v1⟩

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