Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Scientific Reports Année : 2020

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

Pierre Leclerc
  • Fonction : Auteur
Cédric Ray
  • Fonction : Auteur
  • PersonId : 849462
Laure Alston
  • Fonction : Auteur
  • PersonId : 968581
Carole Frindel
Pierre-François Brevet
David Meyronet
David Rousseau
  • Fonction : Auteur
  • PersonId : 1203062

Résumé

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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Dates et versions

hal-02440653 , version 1 (15-01-2020)
hal-02440653 , version 2 (29-01-2020)

Identifiants

  • HAL Id : hal-02440653 , version 1

Citer

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, In press. ⟨hal-02440653v1⟩

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