Abstract
H3K27M mutation is the most common mutation in brainstem gliomas (BSGs), which is related with highly invasive neoplasms and poor prognosis. Accurate presurgical and noninvasive prediction of H3K27M mutations based on preoperative multi-modal neuroimaging is of great clinical value in the diagnosis, prognosis and therapeutic selection of BSGs. Traditional BSG radiomics models usually only focus on tumor local morphometric characteristics. However, given that highly invasive BSGs may significantly affect large-scale brain network connectivity, we reasonably infer that local radiomics and global connectomics may provide different perspectives for H3K27M genotype prediction. Therefore, we define a graph-based diffusion radiomics learning model to integrate these two kinds of features seamlessly. Specifically, edges of the defined brain network are determined by neural fiber connections, while node features of brainstem are governed by local tumor radiomics. Upon this model, we further propose a multi-mechanism diffusion convolutional network to couple multi-modal information and generate a joint representation for brain disease diagnosis. By graph diffusion convolution, the local radiomics information spread along the brain network structure to enhance graph representation learning, and eventually the learned diffusion radiomics features contribute to disease prediction. Experiments on a real BSG dataset demonstrate the effectiveness and advantages of our proposed method for preoperative prediction of H3K27M statuses.
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Acknowledgments
The authors acknowledge supports from Beijing Municipal Natural Science Foundation (7212202), and National Natural Science Foundation of China (82027807, 81771940).
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Yang, N., Xiao, X., Wang, X., Gu, G., Zhang, L., Liao, H. (2021). H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_16
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DOI: https://doi.org/10.1007/978-3-030-87586-2_16
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