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

P. Leclerc, L. Alston, L. Mahieu-Williame, C. Ray, M. Hébert, P. Kantapareddy, C. Frindel, P. F. Brevet, D. Meyronet, J. Guyotat, D. Rousseau, B. Montcel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationClinical and Translational Neurophotonics 2020
EditorsSteen J. Madsen, Victor X. D. Yang, Nitish V. Thakor
ISBN (Electronic)9781510632134
Publication statusPublished - 2019
EventClinical and Translational Neurophotonics 2020 - San Francisco, United States
Duration: 2020 Jan 1 → …

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceClinical and Translational Neurophotonics 2020
Country/TerritoryUnited States
CitySan Francisco
Period20/1/1 → …


  • 5-ALA
  • Fluorescence spectroscopy
  • Glioma
  • Human brain; machine learning; classification
  • Interventional imaging
  • Neurosurgery
  • Protoporphyrin IX


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