Chemistry (miscellaneous); Analytical Chemistry; Organic Chemistry; Physical and Theoretical Chemistry; Molecular Medicine; Drug Discovery; Pharmaceutical Science
Abstract :
[en] 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.
Disciplines :
Oncology Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Klein, Karoline ✱; Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
Klamminger, Gilbert Georg ✱; Department of General and Special Pathology, Saarland University (USAAR), 66424 Homburg, Germany ; Department of General and Special Pathology, Saarland University Medical Center (UKS), 66424 Homburg, Germany ; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg
Jelke, Finn; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg ; Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg
Arroteia, Isabel Fernandes ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
Slimani, Rédouane; Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), 4362 Esch-sur-Alzette, Luxembourg ; Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg ; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
Mirizzi, Giulia; Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
HUSCH, Andreas ; University of Luxembourg ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
FRAUENKNECHT, Katrin ; University of Luxembourg ; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg ; Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg ; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
MITTELBRONN, Michel ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Neuropathology ; National Center of Pathology (NCP), Laboratoire National de Santé (LNS), 3555 Dudelange, Luxembourg ; Luxembourg Center of Neuropathology (LCNP), 3555 Dudelange, Luxembourg ; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg
HERTEL, Frank ; University of Luxembourg ; Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg
Kleine Borgmann, Felix B.; Faculty of Medicine, Saarland University (USAAR), 66424 Homburg, Germany ; National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), 1210 Luxembourg, Luxembourg ; Department of Cancer Research (DoCR), Luxembourg Institute of Health (LIH), 1210 Luxembourg, Luxembourg ; Hôpitaux Robert Schuman, 1130 Luxembourg, Luxembourg
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms
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