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. 2020 Jan 29;10:1462. doi: 10.1038/s41598-020-58299-7

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© 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/.

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Global view of the proposed machine learning-based prediction of glioma margin by PpIX fluorescence spectroscopic measurements. In this study, the data set is composed of 50 samples from 10 patients. From left to right, the optical spectrum of cells around a tumor is measured. The dimension of the spectral information is then reduced to lower the redundancy. Supervised or unsupervised algorithms are finally used to classify the data and create a prediction of tissue state from the PpIX fluorescence spectroscopic measurements.