In iScience
Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.
Li Zhongxiao, Cong Yuwei, Chen Xin, Qi Jiping, Sun Jingxian, Yan Tao, Yang He, Liu Junsi, Lu Enzhou, Wang Lixiang, Li Jiafeng, Hu Hong, Zhang Cheng, Yang Quan, Yao Jiawei, Yao Penglei, Jiang Qiuyi, Liu Wenwu, Song Jiangning, Carin Lawrence, Chen Yupeng, Zhao Shiguang, Gao Xin
2023-Jan-20
Cancer, Machine learning, Pathology
