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

Pierre Leclerc, Cedric Ray, Laurent Mahieu-Williame, Laure Alston, Carole Frindel, Pierre-François Brevet, David Meyronet, Jacques Guyotat, Bruno Montcel, David Rousseau

Scientific reports 2020 Jan 29

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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.

Citation

Pierre Leclerc, Cedric Ray, Laurent Mahieu-Williame, Laure Alston, Carole Frindel, Pierre-François Brevet, David Meyronet, Jacques Guyotat, Bruno Montcel, David Rousseau. Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy. Scientific reports. 2020 Jan 29;10(1):1462