Ying Yu-Zhe, Cai Xiao-Hong, Yang Han, Huang Hua-Wei, Zheng Dao, Li Hao-Yi, Dong Ge-Hong, Wang Yong-Gang, Jiang Zhong-Li, An Zhu-Lin, Zhang Guo-Bin
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Institute of Computing Technology, Chinese Academy of Sciences, Xiamen, China.
Front Oncol. 2025 Jun 6;15:1573700. doi: 10.3389/fonc.2025.1573700. eCollection 2025.
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.
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.
准确区分胶质瘤复发和放射性坏死对于放疗后疑似胶质瘤复发患者的管理至关重要。本研究旨在开发一种基于深度学习的方法，利用常规磁共振成像（MRI）扫描自动鉴别胶质瘤复发和放射性坏死。
我们回顾性研究了234例胶质瘤切除术后接受放疗且在后续MRI检查中出现疑似复发病变的患者。为每位患者采集了包括T1加权、T2加权和对比增强T1（T1ce）序列的常规3D-MRI扫描。在分析的病例中，192例（82.1%）经病理证实为胶质瘤复发，42例（17.9%）被诊断为放射性坏死。采用各种卷积神经网络（CNN）模型从MRI扫描中学习指示胶质瘤复发和放射性坏死的放射学特征。使用敏感性、特异性、准确性和曲线下面积（AUC）等性能评估指标来评估模型的性能。
在评估的CNN模型中，ResNet10表现出最高的敏感性（0.78）、特异性（0.94）、准确性（0.91）和AUC值0.83。此外，MresNet模型达到了最高的特异性（0.980），但敏感性相对较低（0.56）。另一个评估的CNN模型Vgg16的敏感性为0.56，特异性为0.94，准确性为0.88，AUC值为0.70。
所提出的ResNet10 CNN模型在常规MRI扫描中表现出有前景的性能，使其在临床环境中具有高度适用性。这些发现有助于提高使用常规MRI区分胶质瘤复发和放射性坏死的诊断准确性。
