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.
|Original language||English (US)|
|State||Published - Dec 24 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-12-26
Acknowledged KAUST grant number(s): BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, REI/1/4742-01-01, URF/1/4098-01-01, URF/1/4352-01-01
Acknowledgements: This work was supported by Office of Research Administration (ORA) at KAUST under award numbers BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/4098-01-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4352-01-01, and REI/1/4742-01-01. Figure 1 was created by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST).