Predicting 1p/19q chromosomal deletion of brain tumors using machine learning

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dc.authoridEMIROGLU, BULENT GURSEL/0000-0002-1656-6450
dc.authoridCinarer, Gokalp/0000-0003-0818-6746
dc.authoridYURTTAKAL, Ahmet Hasim/0000-0001-5170-6466
dc.contributor.authorCinarer, Gokalp
dc.contributor.authorEmiroglu, Bulent Gursel
dc.contributor.authorYurttakal, Ahmet Hasim
dc.date.accessioned2025-01-21T16:43:35Z
dc.date.available2025-01-21T16:43:35Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAdvances in molecular and genetic technologies have enabled the study of mutation and molecular changes in gliomas. The 1p/19q coding state of gliomas is important in predicting pathogenesis-based pharmacological treatments and determining innovative immunotherapeutic strategies. In this study, T1-weighted and T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images of 121 low-grade glioma patients with biopsy-proven 1p/19q coding status and no deletion (n = 40) or co-deletion (n = 81) were used. First, regions of interests were segmented with the grow-cut algorithm. Later, 851 radiomic features including three-dimensional wavelet preprocessed and non-preprocessed ones were extracted from six different matrices such as first order, shape and texture. The extracted features were preprocessed with the synthetic minority over-sampling technique algorithm. Next, the 1p/19q decoding states of gliomas were classified using machine-learning algorithms. The best classification in the classification of glioma grades (grade II and grade III) according to 1p/19q coding status was obtained by using the logistic regression algorithm, with 93.94% accuracy and 94.74% area under the curve values. In conclusion, it was determined that non-invasive estimation of 1p/19q status from MRI images enables the selection of effective treatment strategies with early diagnosis using machine-learning algorithms without the need for surgical biopsy.
dc.identifier.doi10.1680/jemmr.20.00350
dc.identifier.endpage244
dc.identifier.issn2046-0147
dc.identifier.issn2046-0155
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85109087211
dc.identifier.scopusqualityQ2
dc.identifier.startpage238
dc.identifier.urihttps://doi.org/10.1680/jemmr.20.00350
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25297
dc.identifier.volume10
dc.identifier.wosWOS:000673980400013
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIce Publishing
dc.relation.ispartofEmerging Materials Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectcomputational studies; imaging; processing
dc.titlePredicting 1p/19q chromosomal deletion of brain tumors using machine learning
dc.typeArticle

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