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Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989497/ https://www.ncbi.nlm.nih.gov/pubmed/31996727 http://dx.doi.org/10.1038/s41598-020-58299-7 |
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author | Leclerc, Pierre Ray, Cedric Mahieu-Williame, Laurent Alston, Laure Frindel, Carole Brevet, Pierre-François Meyronet, David Guyotat, Jacques Montcel, Bruno Rousseau, David |
author_facet | Leclerc, Pierre Ray, Cedric Mahieu-Williame, Laurent Alston, Laure Frindel, Carole Brevet, Pierre-François Meyronet, David Guyotat, Jacques Montcel, Bruno Rousseau, David |
author_sort | Leclerc, Pierre |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6989497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69894972020-02-05 Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy Leclerc, Pierre Ray, Cedric Mahieu-Williame, Laurent Alston, Laure Frindel, Carole Brevet, Pierre-François Meyronet, David Guyotat, Jacques Montcel, Bruno Rousseau, David Sci Rep Article 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. Nature Publishing Group UK 2020-01-29 /pmc/articles/PMC6989497/ /pubmed/31996727 http://dx.doi.org/10.1038/s41598-020-58299-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Leclerc, Pierre Ray, Cedric Mahieu-Williame, Laurent Alston, Laure Frindel, Carole Brevet, Pierre-François Meyronet, David Guyotat, Jacques Montcel, Bruno Rousseau, David Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title | Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title_full | Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title_fullStr | Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title_full_unstemmed | Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title_short | Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy |
title_sort | machine learning-based prediction of glioma margin from 5-ala induced ppix fluorescence spectroscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989497/ https://www.ncbi.nlm.nih.gov/pubmed/31996727 http://dx.doi.org/10.1038/s41598-020-58299-7 |
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