Publication:
Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues

dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorBatur, Numan
dc.contributor.kuauthorBaysal, Kemal
dc.contributor.kuauthorBilgin, Buse
dc.contributor.kuauthorEsengür, Ömer Tarık
dc.contributor.kuauthorKulaç, İbrahim
dc.contributor.kuauthorOnbaşlı, Mehmet Cengiz
dc.contributor.kuauthorSolaroğlu, İhsan
dc.contributor.kuauthorTorun, Hülya
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2024-11-09T23:26:24Z
dc.date.issued2022
dc.description.abstractGlioblastoma 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.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipPresidency of Turkey, Presidency of Strategy and Budget
dc.description.sponsorshipScientific 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.sponsorshipThe authors gratefully acknowledge the funding provided by The Scientific and Technological Research Council of Turkey (TUBITAK) under project number 119S362.
dc.description.volume11944
dc.identifier.doi10.1117/12.2608957
dc.identifier.eissn1996-756X
dc.identifier.isbn978-1-5106-4760-2
dc.identifier.isbn978-1-5106-4759-6
dc.identifier.issn0277-786X
dc.identifier.scopus2-s2.0-85131170374
dc.identifier.urihttps://doi.org/10.1117/12.2608957
dc.identifier.urihttps://hdl.handle.net/20.500.14288/11548
dc.identifier.wos812269100005
dc.keywordsRaman spectroscopy
dc.keywordsGlioblastoma
dc.keywordsBrain cancer
dc.keywordsMachine learning
dc.keywordsTissue identification
dc.keywordsWhite matter
dc.keywordsBrain tumor
dc.keywordsRaman spectra classification
dc.language.isoeng
dc.publisherSpie-Int Soc Optical Engineering
dc.relation.ispartofMultiscale Imaging and Spectroscopy III
dc.subjectEngineering
dc.subjectBiomedical engineering
dc.subjectOptics
dc.subjectImaging systems
dc.subjectPhotography
dc.titleMachine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorTorun, Hülya
local.contributor.kuauthorBatur, Numan
local.contributor.kuauthorBilgin, Buse
local.contributor.kuauthorEsengür, Ömer Tarık
local.contributor.kuauthorBaysal, Kemal
local.contributor.kuauthorKulaç, İbrahim
local.contributor.kuauthorSolaroğlu, İhsan
local.contributor.kuauthorOnbaşlı, Mehmet Cengiz
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2School of Medicine
local.publication.orgunit2Graduate School of Sciences and Engineering
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