Spectral complexity of 5-ALA induced PpIX fluorescence in guided surgery: a clinical study towards the discrimination of healthy tissue and margin boundaries in high and low grade gliomas

Alston, Laure; Mahieu-Williame, Laurent; Hébert, Mathieu; Kantapareddy, Pascal; Meyronet, David; Rousseau, D.; Guyotat, Jacques; Montcel, Bruno

doi:10.1364/boe.10.002478

Cited by 40 publications

(94 citation statements)

References 51 publications

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“…Interestingly, while obtained here from a purely data-driven approach, this number of clusters is compatible (see red dotted lines in Fig. 2) with the number of classes proposed independently by the clinical taxonomy described in 23 from histological images: tumor core, high-density margin, low-density margin, healthy tissue. Global view of the proposed machine learning-based prediction of glioma margin by PpIX fluorescence spectroscopic measurements.…”

Section: Resultssupporting

confidence: 80%

“…3). By comparison, in our previous study 23 , for one excitation wavelength, 2 features were extracted from the spectrum: the relative intensity of the component leading to a peak of PpIX fluorescence at 620 nm (PpIX620) and the relative intensity of the component leading to a peak PpIX of fluorescence at 634 nm (PpIX634). With three excitation wavelengths, the resulting feature space of this model is 2 × 3 = 6 descriptors to describe the variability of the data.…”

Section: Identification Of Best Spectral Featuresmentioning

confidence: 99%

“…In this study, we adopt a different approach for the prediction of glioma margin from fluorescence information. Instead of choosing a small amount of numerical features used as biomarkers like in the recent above-cited literature 6,[15][16][17][18][19][20][21][22][23][24] , we propose to investigate the prediction of glioma margin with an entirely data-driven approach where no assumption of feature selection based on expert is made. To this purpose, we implement, for the first time to our knowledge in this context, a machine learning classification approach.…”

mentioning

confidence: 99%

“…1 and detailed in the material and method section predicts glioma margin from the raw fluorescence spectrum. This is done on the same data acquired previously in a surgical procedure of 5-ALA induced PpIX guided glioma removal and which had been only processed so far with a biomarker approach 23 . This choice enables a comparison of the prediction performance of glioma margin from a biomarker approach with a novel expert-independent point of view.…”

mentioning

confidence: 99%

“…This choice enables a comparison of the prediction performance of glioma margin from a biomarker approach with a novel expert-independent point of view. Also, as another element of novelty, the prediction of glioma margin is performed from 3 different fluorescent spectra in response to 3 distinct excitation wavelengths taken successively over the same area, while previously only a few features extracted from a single fluorescent spectrum were used for analysis 23 .…”

mentioning

confidence: 99%

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

Leclerc

Ray

Mahieu-Williame

et al. 2020

Sci Rep

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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 biomarkerbased 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. Gliomas account for more than fifty percent of primitive brain tumors. They are infiltrative tumors, with a margin difficult to identify and discriminate from the surrounding healthy tissues. The world health organization (WHO) classifies gliomas in 4 grades 1 , but most studies commonly consider two separate groups: High-Grade Gliomas (HGG) and Low-Grade Gliomas (LGG). Studies have shown that in 85% cases, recurrences of HGG are localized less than 2 centimeters away from the initial tumor 2. Then, improving the extent of resection is relevant to prevent recurrence and improve life quality and expectancy 3-5. Pre-operative MRI combined with neuro-navigation is currently used in the operating theater 6,7 but shows strong limitations 8-10. 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence microscopy has shown its relevance in neuro-oncology 11. PpIX absorbs light at 405 nm and emits fluorescence with a main peak centered at 634 nm. This technique is the actual clinical standard for PpIX-based surgical assistance. However, its sensitivity is still limited when applied to low-density infiltrative parts of HGG 12,13 or to LGG 14. Various 5-ALA induce PpIX fluorescence spectroscopy methods have been proposed to overcome these sensitivity issues. Previous works 6,15-24 , focus on the extraction of biomarkers from the measurements, based on a priori information on the link between the biomarkers and the microenvironment of PpIX. These approaches are known as expert-based, and various biomarker models have been proposed in the literature. Quantification of PpIX concentration 15 show enhanced sensitivity either in HGG 16 or in LGG 17. Normalization procedures of biomarkers can also increase their robustness 6,18,19. Other works suggest that relevant models could be obtained based on the shape of the PpIX emission spectrum 18-26. These works show that the PpIX fluorescence emission spectral complexity in tissue is closely linked with the pathological status. However, the still unsolved origin of this complexity impairs the extraction of the best features with an expert-based related method, thus preventing the classification of measurements into relevant pathological status.

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Section: Resultssupporting

confidence: 80%

Section: Identification Of Best Spectral Featuresmentioning

confidence: 99%