Browsing by Subject "Tumor margin detection"
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Item Open Access Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy(Bilkent University, 2021-07) Çakmakçı, DorukComplete 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.