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.
Gliomas are diffuse brain tumors still hardly curable due to the difficulties to identify their margins. 5-ALA induced PpIX fluorescence measurements have enabled to gain in sensitivity for discriminating margin from healthy tissue but they remain limited. In this study, we assume that two states of PpIX contribute to total fluorescence. We show that fluorescence in low density margins of high grade gliomas or in low grade gliomas comes mainly from PpIX peak centered at 620 nm. These results could help to improve fluorescence-guided resection of gliomas by discriminating healthy tissues from tumor margins.
We show the feasibility of using an intraoperative spectroscopic device to identify tumors margins during glioma resection. The collected fluorescence spectra is fitted with two reference spectra of PpIX and the contribution of each spectrum enables to overcome the sensitivity of current techniques by seeing tumor margins and low grade gliomas.
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