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

Date
2021-07
Editor(s)
Advisor
Çiçek, A. Ercüment
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
Print ISSN
Electronic ISSN
Publisher
Bilkent University
Volume
Issue
Pages
Language
English
Journal Title
Journal ISSN
Volume Title
Series
Abstract

Complete 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 ma-lign 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 anal-ysis for the existence of known tumor biomarkers can be made and this requires atechnicianwithchemistrybackground, andapathologistwithknowledgeon tumor metabolism to be present during surgery. Here, we show that we can accu-rately 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 and validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an impor-tant role in distinguishing tumor and normal cells and suggest new biomarker regions.

Course
Other identifiers
Book Title
Citation
Published Version (Please cite this version)