Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy
| buir.advisor | Çiçek, A. Ercüment | |
| dc.contributor.author | Çakmakçı, Doruk | |
| dc.date.accessioned | 2021-08-16T11:19:02Z | |
| dc.date.available | 2021-08-16T11:19:02Z | |
| dc.date.copyright | 2021-07 | |
| dc.date.issued | 2021-07 | |
| dc.date.submitted | 2021-08-06 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2021. | en_US |
| dc.description | Includes bibliographical references (leaves 21-26). | en_US |
| dc.description.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. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | by Doruk Çakmakçı | en_US |
| dc.format.extent | x, 46 leaves : charts ; 30 cm. | en_US |
| dc.identifier.itemid | B126715 | |
| dc.identifier.uri | http://hdl.handle.net/11693/76434 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Tumor margin detection | en_US |
| dc.subject | HRMAS NMR | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Feature importance | en_US |
| dc.title | Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy | en_US |
| dc.title.alternative | Beyin tümörü sınırlarının ameliyat sırasında HRMAS NMR spektroskopisi kullanılarak makine öğrenimi destekli değerlendirilmesi | en_US |
| dc.type | Thesis | en_US |