Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

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buir.contributor.authorÇakmakçı, Doruk
buir.contributor.authorKarakaşlar, Emin Onur
buir.contributor.authorÇicek, A. Ercüment
dc.citation.issueNumber11en_US
dc.citation.volumeNumber16en_US
dc.contributor.authorÇakmakçı, Doruken_US
dc.contributor.authorKarakaşlar, Emin Onuren_US
dc.contributor.authorRuhland, E.en_US
dc.contributor.authorChenard, M.-P.en_US
dc.contributor.authorProust, F.en_US
dc.contributor.authorPiotto, M.en_US
dc.contributor.authorNamer, I. J.en_US
dc.contributor.authorÇicek, A. Ercümenten_US
dc.date.accessioned2021-03-01T12:03:27Z
dc.date.available2021-03-01T12:03:27Z
dc.date.issued2020
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractComplete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2021-03-01T12:03:27Z No. of bitstreams: 1 Machine_learning_assisted_intraoperative_assessment_of_brain_tumor_margins_using_HRMAS_NMR_spectroscopy.pdf: 1177742 bytes, checksum: ce31d733323547483e76720c272acbb6 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-01T12:03:27Z (GMT). No. of bitstreams: 1 Machine_learning_assisted_intraoperative_assessment_of_brain_tumor_margins_using_HRMAS_NMR_spectroscopy.pdf: 1177742 bytes, checksum: ce31d733323547483e76720c272acbb6 (MD5) Previous issue date: 2020en
dc.identifier.doi10.1371/journal.pcbi.1008184en_US
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/11693/75673
dc.language.isoEnglishen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttps://dx.doi.org/10.1371/journal.pcbi.1008184en_US
dc.source.titlePLoS Computational Biologyen_US
dc.titleMachine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopyen_US
dc.typeArticleen_US

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