{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Klein K"],"funding":["Luxembourg National Research Fund","Foundation Cancer Luxembourg"],"pagination":["979"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10935394"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["29(5)"],"pubmed_abstract":["Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control."],"journal":["Molecules (Basel, Switzerland)"],"pubmed_title":["Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms."],"pmcid":["PMC10935394"],"funding_grant_id":["FNR PEARL P16/BM/11192868"],"pubmed_authors":["Kleine Borgmann FB","Arroteia IF","Klamminger GG","Slimani R","Husch A","Hertel F","Mombaerts L","Mirizzi G","Klein K","Jelke F","Frauenknecht KBM","Mittelbronn M"],"additional_accession":[]},"is_claimable":false,"name":"Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.","description":"Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2026-06-24T03:09:10.955Z","creation":"2026-06-24T03:06:19.491Z"},"accession":"S-EPMC10935394","cross_references":{"pubmed":["38474491"],"doi":["10.3390/molecules29050979"]}}