Abstract
Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated with molecular features following the latest WHO criteria. We first propose a novel over-segmentation strategy for region-of-interest (ROI) selection in large histopathology whole slide images (WSIs). A Deep Neural Network (DNN)-based classification method then fuses molecular features with cellularity features to improve tumor classification performance. We evaluate the proposed method with 549 patient cases from The Cancer Genome Atlas (TCGA) dataset for evaluation. The cross validated classification accuracies are 93.81% for lower-grade glioma (LGG) and high-grade glioma (HGG) using a regular DNN, and 73.95% for LGG II and LGG III using a residual neural network (ResNet) DNN, respectively. Our experiments suggest that the type of deep learning has a significant impact on tumor subtype discrimination between LGG II vs. LGG III. These results outperform state-of-the-art methods in classifying LGG II vs. LGG III and offer competitive performance in distinguishing LGG vs. HGG in the literature. In addition, we also investigate molecular subtype classification using pathology images and cellularity information. Finally, for the first time in literature this work shows promise for cellularity quantification to predict brain tumor grading for LGGs with IDH mutations.
Highlights
Gliomas are primary brain tumors that originate from glial cells
We propose a deep neural networks (DNN)-based method for brain tumor classification and grading using both pathology and molecular data following the latest 2016 World Health Organization (WHO) classification criteria
The classification method, for the first time in literature, integrates a cellularity feature which is derived from morphology of brain tumor histopathology images to improve the performance
Summary
Gliomas are primary brain tumors that originate from glial cells. Survival in patients with gliomas is dependent on the tumor type and grade. Yonekura et al proposed an improved disease stage classification with a convolutional neural network for glioma histopathology images [21]. They obtain classification accuracy of 87.15% for differentiating LGG and HGG. Even though histopathology-based tumor grade classification has been the standard of care, there can be high intra- or inter-observer variability [4, 23] Because of this variability in tumor grade classification using only tumor morphology, the updated WHO integrated genetic information to better classify gliomas and help guide clinical decision-making for treatment planning and management of tumor patients [7, 10, 24, 25]
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