Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images
Weiwei Wang, Yuanshen Zhao, Lianghong Teng, Jing Yan, Yang Guo, Yuning Qiu, Yuchen Ji, Bin Yu, Dongling Pei, Wenchao Duan, Minkai Wang, Li Wang, Jingxian Duan, Qiuchang Sun, Shengnan Wang, Huanli Duan, Chen Sun, Yu Guo, Lin Luo, Zhixuan Guo, Fangzhan Guan, Zilong Wang, Aoqi Xing, Zhongyi Liu, Hongyan Zhang, Li Cui, Lan Zhang, Guozhong Jiang, Dongming Yan, Xianzhi Liu, Hairong Zheng, Dong Liang, Wencai Li, Zhicheng Li, Zhenyu Zhang
Abstract
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.