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

doi:10.3389/fonc.2025.1573700

	Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI
	Ying, Yu-Zhe 1; Cai, Xiao-Hong 2,3; Yang, Han 2,3; Huang, Hua-Wei 4; Zheng, Dao 1; Li, Hao-Yi 1; Dong, Ge-Hong 5; Wang, Yong-Gang 1; Jiang, Zhong-Li 1; An, Zhu-Lin 2,3; Zhang, Guo-Bin 1
	2025-06-06
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
卷号	15 页码:12
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.
关键词	glioma recurrence radiation necrosis convolutional neural network magnetic resonance imaging deep learning
DOI	10.3389/fonc.2025.1573700
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Administration of Hospitals Incubating Program[PX2023018]
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:001511513600001
出版者	FRONTIERS MEDIA SA
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42360
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.Chinese Acad Sci, Inst Comp Technol, Xiamen, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China 4.Capital Med Univ, Beijing Tiantan Hosp, Dept Crit Care Med, Beijing, Peoples R China 5.Capital Med Univ, Beijing Tiantan Hosp, Dept Pathol, Beijing, 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.