Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma

Authors:
raw_author_name: Weiwei Wang
raw_affiliation_string: Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Yuchen Ji
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Yang Guo
raw_affiliation_string: Department of Neurosurgery, Henan Provincial Hospital, Zhengzhou, China
raw_author_name: Jingxian Duan
raw_affiliation_string: Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Xianzhi Liu
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Dongming Yan
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Dong Liang
raw_affiliation_string: Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; National Innovation Center for Advanced Medical Devices, Shenzhen, China
raw_author_name: Wencai Li
raw_affiliation_string: Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Zhenyu Zhang
raw_affiliation_string: Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
raw_author_name: Zhi-Cheng Li
raw_affiliation_string: Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; National Innovation Center for Advanced Medical Devices, Shenzhen, China

Abstract: Isocitrate dehydrogenase gene (<i>IDH</i>) mutation is one of the most important molecular markers of glioma. Accurate detection of <i>IDH</i> status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. A clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect <i>IDH</i> mutation status from annotation-free hematoxylin and eosin–stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, we also assessed a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. We further assessed the hybrid model's ability in <i>IDH</i> detection between difficult subgroups with different <i>IDH</i> status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. Our data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of <i>IDH</i> mutation from whole slide images in adult patients with diffuse gliomas.