ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1573700
Development and Validation of a Deep Learning Algorithm for Discriminating Glioma Recurrence from Radiation Necrosis on MRI
Provisionally accepted
Yuzhe Ying1
Xiao-Hong Cai2Han Yang2Hua-Wei Huang1Dao Zheng1
Hao-Yi Li1
Gehong Dong1
Yonggang Wang1Zhong-Li Jiang1Zhu-Lin An2*
Guo-Bin Zhang1*- 1Beijing Tiantan Hospital, Capital Medical University, Beijing, Beijing Municipality, China
- 2Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, Beijing Municipality, China
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Purpose: Accurate differentiation between glioma recurrence and radiation necrosis is critical for the management of patients suspected of glioma recurrence following radiation therapy. This study aims to develop a deep learning-based methodology for automated discrimination between glioma recurrence and radiation necrosis using routine magnetic resonance imaging (MRI) scans.We retrospectively investigated 234 patients who underwent radiotherapy after glioma resection and presented with suspected recurrent lesions during follow-up MRI examinations. Routine 3D-MRI scans, including T1-weighted, T2-weighted, and contrast-enhanced T1 (T1ce) sequences, were acquired for each patient. Among the analyzed cases, 192 (82.1%) were pathologically confirmed as glioma recurrence, while 42 (17.9%) were diagnosed as radiation necrosis. Various Convolutional Neural Network (CNN) models were employed to learn radiological features indicative of glioma recurrence and radiation necrosis from the MRI scans. Performance evaluation metrics, such as sensitivity, specificity, accuracy, and area under the curve (AUC), were used to assess the models' performance.Result: Among the evaluated CNN models, ResNet10 demonstrated the highest sensitivity (0.78), specificity (0.94), accuracy (0.91), and an AUC value of 0.83.Additionally, the MresNet model achieved the highest specificity (0.980) but exhibited a relatively lower sensitivity (0.56). Another evaluated CNN model, Vgg16, showed a sensitivity of 0.56, specificity of 0.94, accuracy of 0.88, and an AUC value of 0.70.The proposed ResNet10 CNN model demonstrates promising performance on routine MRI scans, rendering it highly applicable in clinical settings.These findings contribute to enhancing the diagnostic accuracy for distinguishing between glioma recurrence and radiation necrosis using routine MRI.
Keywords: Glioma recurrence, Radiation necrosis, Convolutional Neural Network, Magnetic Resonance Imaging, deep learning CNN=Convolutional Neural Network, AUC=Area Under the Receiver Operating Characteristic Curve, GBM = glioblastoma, PWI=Perfusion-Weighted Imaging
Received: 10 Feb 2025; Accepted: 26 May 2025.
Copyright: © 2025 Ying, Cai, Yang, Huang, Zheng, Li, Dong, Wang, Jiang, An and Zhang. 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:
Zhu-Lin An, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, Beijing Municipality, China
Guo-Bin Zhang, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, Beijing Municipality, 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.