Publication: Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues
dc.contributor.department | Department of Electrical and Electronics Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.department | School of Medicine | |
dc.contributor.kuauthor | Batur, Numan | |
dc.contributor.kuauthor | Baysal, Kemal | |
dc.contributor.kuauthor | Bilgin, Buse | |
dc.contributor.kuauthor | Esengür, Ömer Tarık | |
dc.contributor.kuauthor | Kulaç, İbrahim | |
dc.contributor.kuauthor | Onbaşlı, Mehmet Cengiz | |
dc.contributor.kuauthor | Solaroğlu, İhsan | |
dc.contributor.kuauthor | Torun, Hülya | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.contributor.schoolcollegeinstitute | SCHOOL OF MEDICINE | |
dc.date.accessioned | 2024-11-09T23:26:24Z | |
dc.date.issued | 2022 | |
dc.description.abstract | Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an error-free diagnosis of glioblastoma in clinics. Here, we report on our clinical test results of Raman spectroscopy and machine learning-based glioblastoma identification methodology for a cohort of 20 glioblastoma and 18 white matter tissue samples. We used Raman spectroscopy to distinguish FFPE glioblastoma and white matter tissues applying our previously reported protocols about optimized FFPE sample preparation and Raman measurement parameters. One may analyze the composition and identify the subtype of brain tumors using Raman spectroscopy since this technique yields detailed molecule-specific information from tissues. We measured and classified the Raman spectra of neoplastic and non-neoplastic tissue sections using machine learning classifiers including support vector machine and random forest with 86.6% and 83.3% accuracies, respectively. These proof-of-concept results demonstrate that this technique might be eventually used in the clinics to assist pathologists once validated with a larger and more diverse glioblastoma cohort and improved detection accuracies. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.openaccess | NO | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | TÜBİTAK | |
dc.description.sponsorship | Presidency of Turkey, Presidency of Strategy and Budget | |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) [119S362] The authors gratefully acknowledge the use of the services and facilities of the Koc University Research Center for Translational Medicine (KUTTAM), funded by the Presidency of Turkey, Presidency of Strategy and Budget. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Presidency of Strategy and Budget. | |
dc.description.sponsorship | The authors gratefully acknowledge the funding provided by The Scientific and Technological Research Council of Turkey (TUBITAK) under project number 119S362. | |
dc.description.volume | 11944 | |
dc.identifier.doi | 10.1117/12.2608957 | |
dc.identifier.eissn | 1996-756X | |
dc.identifier.isbn | 978-1-5106-4760-2 | |
dc.identifier.isbn | 978-1-5106-4759-6 | |
dc.identifier.issn | 0277-786X | |
dc.identifier.scopus | 2-s2.0-85131170374 | |
dc.identifier.uri | https://doi.org/10.1117/12.2608957 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/11548 | |
dc.identifier.wos | 812269100005 | |
dc.keywords | Raman spectroscopy | |
dc.keywords | Glioblastoma | |
dc.keywords | Brain cancer | |
dc.keywords | Machine learning | |
dc.keywords | Tissue identification | |
dc.keywords | White matter | |
dc.keywords | Brain tumor | |
dc.keywords | Raman spectra classification | |
dc.language.iso | eng | |
dc.publisher | Spie-Int Soc Optical Engineering | |
dc.relation.ispartof | Multiscale Imaging and Spectroscopy III | |
dc.subject | Engineering | |
dc.subject | Biomedical engineering | |
dc.subject | Optics | |
dc.subject | Imaging systems | |
dc.subject | Photography | |
dc.title | Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues | |
dc.type | Conference Proceeding | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Torun, Hülya | |
local.contributor.kuauthor | Batur, Numan | |
local.contributor.kuauthor | Bilgin, Buse | |
local.contributor.kuauthor | Esengür, Ömer Tarık | |
local.contributor.kuauthor | Baysal, Kemal | |
local.contributor.kuauthor | Kulaç, İbrahim | |
local.contributor.kuauthor | Solaroğlu, İhsan | |
local.contributor.kuauthor | Onbaşlı, Mehmet Cengiz | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit1 | SCHOOL OF MEDICINE | |
local.publication.orgunit2 | Department of Electrical and Electronics Engineering | |
local.publication.orgunit2 | School of Medicine | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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