From pixels to prognosis: Leveraging radiomics and machine learning to predict IDH1 genotype in gliomas
Yazarlar (6)
Doç. Dr. Aslı Beril KARAKAŞ TANIR Kastamonu Üniversitesi, Türkiye
Prof. Dr. Figen Govsa Ege Üniversitesi, Türkiye
Prof. Dr. Mehmet Asım Özer Ege Üniversitesi, Türkiye
Doç. Dr. Hüseyin Biçeroğlu Ege Üniversitesi, Türkiye
Doç. Dr. Cenk Eraslan Ege Üniversitesi, Türkiye
Deniz Tanır Kafkas Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | NEUROSURGICAL REVIEW (Q1) | ||
| Dergi ISSN | 0344-5607 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2025 |
| Cilt / Sayı / Sayfa | 48 / 1 / 1–26 | DOI | 10.1007/s10143-025-03515-z |
| Makale Linki | https://doi.org/10.1007/s10143-025-03515-z | ||
| UAK Araştırma Alanları |
Anatomi
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| Özet |
| Gliomas are the most common primary tumors of the central nervous system, and advances in genetics and molecular medicine have significantly transformed their classification and treatment. This study aims to predict the IDH1 genotype in gliomas using radiomics and machine learning (ML) methods. Retrospective data from 108 glioma patients were analyzed, including MRI data supported by demographic details such as age, sex, and comorbidities. Tumor segmentation was manually performed using 3D Slicer software, and 112 radiomic features were extracted with the PyRadiomics library. Feature selection using the mRMR algorithm identified 17 significant radiomic features. Various ML algorithms, including KNN, Ensemble, DT, LR, Discriminant and SVM, were applied to predict the IDH1 genotype. The KNN and Ensemble models achieved the highest sensitivity (92-100%) and specificity (100%), emerging … |
| Anahtar Kelimeler |
| Glioma | IDH1 | Radiomics | Machine learning (ML) | K-Nearest Neighbor (KNN) | Support Vector Machine (SVM) | Magnetic Resonance Imaging (MRI) |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Google Scholar | 6 |