5-ALA-induced PpIX fluorescence in gliomas resection: spectral complexity of the emission spectrum in the infiltrative compound

Montcel, Bruno; Mahieu-Williame, Laurent; Armoiry, Xavier; Meyronet, David; Guyotat, Jacques

doi:10.1117/12.2033472

Cited by 7 publications

(20 citation statements)

References 13 publications

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“…However, the relevance of the blue-shift effect is retrieved in this data-driven approach. This reinforces the expert-driven previous works [18][19][20][21][22][23][24][25][26] focusing on the blue-shift. The biological mechanism behind this shift remains to be uncovered to this day and is not the subject of this work.…”

Section: Discussionsupporting

confidence: 88%

“…Other works already suggested a wavelength blue-shift of the peak intensity of the emission spectrum correlated with the tissue pathological status 18,19,23 . In particular, a second peak of fluorescence at 620 nm has been observed in tissues [19][20][21][22][23]25,26 or in cell culture 21,24 . It is in vivo origin is still an open issue, some works supporting the assumption that it is related to different aggregates of PpIX 19,23,24,26 , other works 20,35 argue that it is a fluorescence induced www.nature.com/scientificreports www.nature.com/scientificreports/ by the precursors of PpIX, uroporphyrins or coproporphyrins.…”

Section: Discussionmentioning

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: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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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“…Existing approaches are effective when the emission spectral band of the baseline is far from the one of the PpIX. 8,[10][11][12] The baseline is modeled by a Gaussian function, 13,14 or expert dependent weighted sum, which contain a finite number of fluorophores whose fluorescence spectral shape is assumed known. 8,15 However, fluorophores that overlap with PpIX spectral emission band are difficult to consider.…”

Section: Introductionmentioning

confidence: 99%

Experimental decorrelation requirement for robust estimation of fluorophores using multiple-wavelength excitation fluorescence spectroscopy

Gautheron,

Sdika,

Guyotat

et al. 2023

Diffuse Optical Spectroscopy and Imaging IX

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Protoporphyrin IX (PpIX) is a fluorophore being currently used to localize tumoral tissues. The tissue is usually excited at one wavelength, e.g., 405 nm, and the fluorescence signal is used to estimate the amount of PpIX during surgery. However, other fluorophores (baseline) whose emission spectra are close to the one of PpIX impair the quantification of PpIX and consequently the tissue pathological status classification. An efficient multi-excitation wavelengths method, free from any a priori on the baseline shape, has been proposed to cope with this issue. This method requires decorrelated measurements in the range of PpIX emission at multiple excitation wavelengths. We investigated the influence of the source bandwith on this decorelation by comparing two experimental setups using either LED or laser diode sources. The experimental setup using laser diodes for excitation increases the decorrelation by 35.3 % compared to the one using LEDs in the spectral range of PpIX emission.

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“…A second photo-state of PpIX peaking at 620 nm (PpIX 620 ) was proposed as a fluorophore in glioma, contributing to the PpIX fluorescence emission (Montcel et al, 2013). A PpIX 620/634 ratio was described in vivo, which increases toward the infiltration zone and decreases toward the tumor core (Montcel et al, 2013;Alston et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Unraveling the blue shift in porphyrin fluorescence in glioma: The 620 nm peak and its potential significance in tumor biology

Suero Molina,

Black,

Walke

et al. 2023

Front. Neurosci.

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In glioma surgery, the low-density infiltration zone of tumors is difficult to detect by any means. While, for instance, 5-aminolevulinic acid (5-ALA)-induced fluorescence is a well-established surgical procedure for maximizing resection of malignant gliomas, a cell density in tumor tissue of 20–30% is needed to observe visual fluorescence. Hyperspectral imaging is a powerful technique for the optical characterization of brain tissue, which accommodates the complex spectral properties of gliomas. Thereby, knowledge about the signal source is essential to generate specific separation (unmixing) procedures for the different spectral characteristics of analytes and estimate compound abundances. It was stated that protoporphyrin IX (PpIX) fluorescence consists mainly of emission peaks at 634 nm (PpIX634) and 620 nm (PpIX620). However, other members of the substance group of porphyrins fluoresce similarly to PpIX due to their common tetrapyrrole core structure. While the PpIX634 signal has reliably been assigned to PpIX, it has not yet been analyzed if PpIX620 might result from a different porphyrin rather than being a second photo state of PpIX. We thus reviewed more than 200,000 spectra from various tumors measured in almost 600 biopsies of 130 patients. Insufficient consideration of autofluorescence led to artificial inflation of the PpIX620 peak in the past. Recently, five basis spectra (PpIX634, PpIX620, flavin, lipofuscin, and NADH) were described and incorporated into the analysis algorithm, which allowed more accurate unmixing of spectral abundances. We used the improved algorithm to investigate the PpIX620 signal more precisely and investigated coproporphyrin III (CpIII) fluorescence phantoms for spectral unmixing. Our findings show that the PpIX634 peak was the primary source of the 5-ALA-induced fluorescence. CpIII had a similar spectral characteristic to PpIX620. The supplementation of 5-ALA may trigger the increased production of porphyrins other than PpIX within the heme biosynthesis pathway, including that of CpIII. It is essential to correctly separate autofluorescence from the main PpIX634 peak to analyze the fluorescence signal. This article highlights the need for a comprehensive understanding of the spectral complexity in gliomas and suggests less significance of the 620 nm fluorescence peak for PpIX analysis and visualization.

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5-ALA-induced PpIX fluorescence in gliomas resection: spectral complexity of the emission spectrum in the infiltrative compound

Cited by 7 publications

References 13 publications

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

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

Experimental decorrelation requirement for robust estimation of fluorophores using multiple-wavelength excitation fluorescence spectroscopy

Unraveling the blue shift in porphyrin fluorescence in glioma: The 620 nm peak and its potential significance in tumor biology

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