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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1518815

Magnetic resonance imaging-based deep learning for predicting subtypes of glioma

Provisionally accepted
Zhen Yang Zhen Yang 1,2Peng Zhang Peng Zhang 1Yi Ding Yi Ding 1Liyi Deng Liyi Deng 2Tong Zhang Tong Zhang 2Yong Liu Yong Liu 2*
  • 1 The Second People's Hospital of Hefei, Hefei, Anhui Province, China
  • 2 The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China

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

    Purpose: To explore the value of deep learning based on magnetic resonance imaging (MRI) in the classification of glioma subtypes.Methods: This study retrospectively included 747 adult patients with surgically pathologically confirmed gliomas from a public database and 64 patients from our hospital. Patients were classified into IDH-wildtype (IDHwt) (490 cases), IDH-mutant/1p19q-noncodeleted (IDHmut-intact) (105 cases), and IDH-mutant/1p19q-codeleted (IDHmut-codel) (216 cases) based on their pathological findings, with the public database of patients were divided into training and validation sets, and patients from our hospital were used as an independent test set. The models were developed based on five categories of preoperative T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted and T2-weighted fluid-attenuated inversion recovery (T1w, T1c, T2w and FLAIR) magnetic resonance imaging (MRI)of four sequences and mixed imaging of the four sequences respectively. The receiver operating characteristic curve (ROC), area under the curve (AUC) of the ROC were generated in the jupyter notebook tool using python language to evaluate the accuracy of the models in classification and comparing the predictive value of different MRI sequences.Results: IDHwt, IDHmut-intact and IDHmut-codel were the best classified in the model containing only FLAIR sequences, with test set AUCs of 0.790, 0.737 and 0.820, respectively; and the worst classified in the model containing only T1w sequences, with test set AUCs of 0.621, 0.537 and 0.760, respectively.Conclusion: We have developed a set of models that can effectively classify glioma subtypes and that work best when only the FLAIR sequence model is included.

    Keywords: deep learning, IDH, Glioma, 1p/19q, MRI

    Received: 29 Oct 2024; Accepted: 16 Jan 2025.

    Copyright: © 2025 Yang, Zhang, Ding, Deng, Zhang and Liu. 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: Yong Liu, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, 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.