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Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI

文献类型：期刊论文

作者Ying, Yu-Zhe1; Cai, Xiao-Hong4,5; Yang, Han4,5; Huang, Hua-Wei3; Zheng, Dao1; Li, Hao-Yi1; Dong, Ge-Hong2; Wang, Yong-Gang1; Jiang, Zhong-Li1; An, Zhu-Lin4,5
刊名FRONTIERS IN ONCOLOGY
出版日期2025-06-06
卷号15页码:12
关键词glioma recurrence radiation necrosis convolutional neural network magnetic resonance imaging deep learning
ISSN号2234-943X
DOI10.3389/fonc.2025.1573700
英文摘要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.Method 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.Conclusion 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.
资助项目Beijing Municipal Administration of Hospitals Incubating Program[PX2023018]
WOS研究方向Oncology
语种英语
WOS记录号WOS:001511513600001
出版者FRONTIERS MEDIA SA
源URL[http://119.78.100.204/handle/2XEOYT63/42359]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者An, Zhu-Lin; Zhang, Guo-Bin
作者单位1.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China
2.Capital Med Univ, Beijing Tiantan Hosp, Dept Pathol, Beijing, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Dept Crit Care Med, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Xiamen, Peoples R China
推荐引用方式
GB/T 7714		 Ying, Yu-Zhe,Cai, Xiao-Hong,Yang, Han,et al. Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI[J]. FRONTIERS IN ONCOLOGY,2025,15:12.
APA Ying, Yu-Zhe.,Cai, Xiao-Hong.,Yang, Han.,Huang, Hua-Wei.,Zheng, Dao.,...&Zhang, Guo-Bin.(2025).Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.FRONTIERS IN ONCOLOGY,15,12.
MLA Ying, Yu-Zhe,et al."Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI".FRONTIERS IN ONCOLOGY 15(2025):12.

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来源：计算技术研究所

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