Deep Learning Model for Intracranial Hemangiopericytoma and Meningioma Classification - Chen, Ziyan; Ye, Ningrong; Jiang, Nian; Yang, Qi; Wanggou, Siyi; Li, Xuejun (Frontiers in Oncology, 2022)
Background Intracranial hemangiopericytoma/solitary fibrous tumor (SFT/HPC) is a rare type of neoplasm containing malignancies of infiltration, peritumoral edema, bleeding, or bone destruction. However, SFT/HPC has similar radiological characteristics as meningioma, which had different clinical managements and outcomes. This study aims to discriminate SFT/HPC and meningioma via deep learning approaches based on routine pre-operative MRI. Methods We enrolled 236 patients with histopathological diagnosis of SFT/HPC (n = 144) and meningioma (n = 122) from 2010 to 2020 in Xiangya hospital. Radiological features were extracted manually and a radiological diagnostic model was applied for classification. And a deep learning pre-trained model ResNet-50 was adapted to train T1-contrast images for predicting tumor class. Deep learning model attention mechanism was visualized by class activation maps. Results Our study reports, that SFT/HPC was found to have more invasion to venous sinus (p = 0.001), more cystic components (p < 0.001), and more heterogeneous enhancement patterns (p < 0.001). Deep learning model achieved a high classification accuracy of 0.889 with AUC of 0.91 in the validation set. Feature maps showed distinct clustering of SFT/HPC and meningioma in the training and test cohorts, respectively. And the attention of the deep learning model mainly focused on the tumor bulks which represented the solid texture features of both tumors for discrimination.