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

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 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 per

SUBMITTER: Cakmakci D 

PROVIDER: S-EPMC7682900 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature