Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Leclerc P

PROVIDER: S-EPMC6989497 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Leclerc Pierre P Ray Cedric C Mahieu-Williame Laurent L Alston Laure L Frindel Carole C Brevet Pierre-François PF Meyronet David D Guyotat Jacques J Montcel Bruno B Rousseau David D

Scientific reports 20200129 1

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 h ...[more]

PMID: 31996727

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Project description:5-Aminolevulinic acid (5-ALA) induced fluorescence to augment surgical resection for high grade glioma has become a standard of care. Protoporphyrin IX (PpIX) visibility is however subject to the variability of the single tumor expression and to the interobserver interpretation. We therefore hypothesized that in different glioma cell lines with variable 5-ALA induced fluorescence, the signal can be pharmacologically increased. We therefore analyzed in three different GBM cell lines, with different expression of epidermal growth factor receptor (EGFR), the variability of 5-ALA induced PpIX fluorescence after the pharmacological blockade at different steps of PpIX breakdown and influencing the outbound transport of PpIX. Using flow cytometry, fluorescence microplate reader, and confocal microscopy the PpIX fluorescence was analyzed after exposure to tin protoporphyrin IX (SnPP), deferoxamine (DFO), and genistein. We furthermore constructed a microscope (Qp9-microscope) being able to measure quantitatively the concentration of PpIX. These values were compared with the extraction of PpIX in tumor biopsy taken during the GBM surgery. Although all three cell lines showed an increase to 5-ALA induced fluorescence their baseline activity was different. Treatment with either SnPP, DFO and genistein was able to increase 5-ALA induced fluorescence. Qp9-microscopy of tumor sample produced a color coded PpIX concentration map which was overlaid on the tumor image. The PpIX extraction from tumor sample analyzed using the plate reader gave lower values of the concentration, as compared to the expected values of the Qp9-microscope, however still in the same decimal range of ?g/mL. This may be due to homogenization of the values during extraction and cell disaggregation. In conclusion pharmacological augmentation in GBM cell lines of PpIX signal is possible. A quantitative PpIX map for surgery is feasible and may help refine surgical excision. Further correlations of tumor tissue samples and Qp9-microscopy is needed, prior to develop an intraoperative surgical adjunct to the already existing 5-ALA induced surgery.

Project description:Simple Summary 5-aminolevulinic acid (5-ALA)-induced PpIX fluorescence is used in neurosurgery for intraoperative identification of high-grade glioma tissue. In this paper, using a fluorescence microscopy analysis on human tumor specimens, we assessed the actual number of fluorescence-positive tumor cells both in low-grade and high-grade glioma, and the ability of 5-ALA to cross the blood–brain barrier (BBB). We found that in high-grade gliomas, 32.7–75.5 percent of cells display 5-ALA induced PpIX fluorescence, whereas in low-grade gliomas the tumor cells did not fluoresce following 5-ALA. Immunofluorescence for BBB components suggested that 5-ALA does not cross the un-breached BBB. These findings are of crucial importance in planning neurosurgical resection of gliomas. Abstract 5-aminolevulinic acid (5-ALA)-induced PpIX fluorescence is used by neurosurgeons to identify the tumor cells of high-grade gliomas during operation. However, the issue of whether 5-ALA-induced PpIX fluorescence consistently stains all the tumor cells is still debated. Here, we assessed the cytoplasmatic signal of 5-ALA by fluorescence microscopy in a series of human gliomas. As tumor markers, we used antibodies against collapsin response-mediated protein 5 (CRMP5), alpha thalassemia/mental retardation syndrome X-linked (ATRX), and anti-isocitrate dehydrogenase 1 (IDH1). In grade III–IV gliomas, the signal induced by 5-ALA was detected in 32.7–75.5 percent of CRMP5-expressing tumor cells. In low-grade gliomas (WHO grade II), the CRMP5-expressing tumor cells did not fluoresce following 5-ALA. Immunofluorescence with antibodies that stain various components of the blood–brain barrier (BBB) suggested that 5-ALA does not cross the un-breached BBB, in spite of its small dimension. To conclude, 5-ALA-induced PpIX fluorescence has an established role in high-grade glioma surgery, but it has limited usefulness in surgery for low-grade glioma, especially when the BBB is preserved.

Dataset Information

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

Publications

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

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