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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1439598

Prediction of TERT mutation status in gliomas using conventional MRI radiogenomic features

Provisionally accepted
Tang Chuyun Tang Chuyun 1*Ling Chen Ling Chen 2*Xu Yifan Xu Yifan 1Huang Lixuan Huang Lixuan 1Zeng Zisan Zeng Zisan 1
  • 1 Department of Radiology, First Affiliated Hospital, Guangxi Medical University, Nanning, China
  • 2 Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou,Guangxi, China

The final, formatted version of the article will be published soon.

    Objective:Telomerase reverse transcriptase (TERT)promoter mutation status in gliomas is a key determinant of treatment strategy and prognosis. This study aimed to analyze the radiogenomic features and construct radiogenomic models utilizing medical imaging techniques to predict the TERT promoter mutation status in gliomas.This was a retrospective study of 304 patients with gliomas. T1-weighted contrast-enhanced, apparent diffusion coefficient, and diffusion-weighted imaging MRI sequences were used for radiomic feature extraction. A total of 3948 features were extracted from MRI images using the FAE software. These included 14 shape features, 18 histogram features, 24 grey level run length matrix, 14 grey level dependence matrix, 16 grey level run length matrix, 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix, and 744 wavelet transforms. The dataset was randomly divided into training and testing sets in a ratio of 7:3. Three feature selection methods and six classification algorithms were used to model the selected features. Predictive performance was evaluated using receiver operating characteristic curve analysis.Results: Among the evaluated classification algorithms, the combination model of recursive feature elimination (RFE) with linear regression (LR) using six features showed the best diagnostic performance (area under the curve: 0.733, 0.562, and 0.633 in the training, validation, and testing sets, respectively). The next best-performing models were naive Bayes, linear discriminant analysis, autoencoder, and support vector machine. Regarding the three feature selection algorithms, RFE showed the most consistent performance, followed by relief and ANOVA. T1-enhanced entropy and GLSZM derived from T1-enhanced images were identified as the most critical radiomics features for distinguishing TERT promoter mutation status.The LR and LRLasso models, mainly based on T1-enhanced entropy and GLSZM, showed good predictive ability for TERT promoter mutations in gliomas using radiomics models.

    Keywords: Glioma, Magnetic Resonance Imaging, TERT promoter mutation, Radiomics, machine learning

    Received: 28 May 2024; Accepted: 15 Jul 2024.

    Copyright: © 2024 Chuyun, Chen, Yifan, Lixuan and Zisan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Tang Chuyun, Department of Radiology, First Affiliated Hospital, Guangxi Medical University, Nanning, China
    Ling Chen, Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou,Guangxi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.