5-ALA induced PpIX fluorescence guided surgery of gliomas: comparison of expert and machine learning based models - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

5-ALA induced PpIX fluorescence guided surgery of gliomas: comparison of expert and machine learning based models

Résumé

Gliomas are diffuse brain tumors still hardly curable due to the difficulties to identify margins. 5-ALA induced PpIX fluorescence measurements enable to gain in sensitivity but are still limited to discriminate margin from healthy tissue. In this fluorescence spectroscopic study, we compare an expert-based model assuming that two states of PpIX contribute to total fluorescence and machine learning-based models. We show that machine learning retrieves the main features identified by the expert approach. We also show that machine learning approach slightly overpasses expert-based model for the identification of healthy tissues. These results might help to improve fluorescence-guided resection of gliomas by discriminating healthy tissues from tumor margins.
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Dates et versions

hal-02928688 , version 1 (02-09-2020)

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Pierre Leclerc, Laure Alston, Laurent Mahieu-Williame, Cédric Ray, Mathieu Hébert, et al.. 5-ALA induced PpIX fluorescence guided surgery of gliomas: comparison of expert and machine learning based models. Clinical and Translational Neurophotonics 2020, Feb 2019, San Francisco, United States. pp.13, ⟨10.1117/12.2546670⟩. ⟨hal-02928688⟩
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