Predicting 1p/19q chromosomal deletion of brain tumors using machine learning
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.
Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. He completed his PhD in Computer Engineering at Kırıkkale University between 2017-2021 and wrote his thesis on "Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms".
His research interests focus on artificial intelligence, machine learning, image processing and deep neural networks. He has carried out important academic studies using artificial intelligence applications in different fields such as medicine, agriculture, food technologies and environmental analysis.
Since 2021, he has been continuing his administrative duties as the Head of the Department of Information Processing and Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses such as Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, Artificial Intelligence Applications.
His academic publications include more than 75 articles scanned by SCI-Expanded, SSCI and Scopus, and international conference proceedings. His work covers various topics such as brain tumor analysis with artificial intelligence, artificial intelligence applications in agriculture, environmental data analysis and object detection.