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PAGE-Net

architecture

 Pathology-Genomic Net(PAGE-Net) integrates histopathological images and genomic data for survival prediction, PAGE-Net not only to improve survival prediction, but also to identify genetic and histopathological patterns that cause different survival rates in patients. PAGE-Net consists of pathology/genome/demography-specific layers, each of which provides comprehensive biological interpretation. In particular, we propose a novel patch-wise texture-based convolutional neural network, with a patch aggregation strategy, to extract global survival-discriminative features, without manual annotation for the pathology-specific layers. We adapted the pathway-based sparse deep neural network, named Cox-PASNet, for the genome-specific layers. The proposed deep learning model was assessed with the histopathological images and the gene expression data of Glioblastoma Multiforme (GBM) at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). PAGE-Net achieved a C-index of 0.702, which is higher than the results achieved with only histopathological images (0.509) and Cox-PASNet (0.640). More importantly, PAGE-Net can simultaneously identify histopathological and genomic prognostic factors associated with patients’ survivals.

This study was published in PSB 2020:
J. Hao, S. Kosaraju, N. Tsaku, D. H. Song, and M. Kang, "PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data", Pacific Symposium on Biocomputing (PSB), 2020 (* indicates the co-first authors)  


Citation

@inbook{doi:10.1142/9789811215636_0032,
     authors = {Hao, Jie and Kosaraju, Sai Chandra and Tsaku, Nelson Zange and Song, Dae Hyun
                and Kang, Mingon},			
     title = {PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using
              Histopathological Images and Genomic Data}},
     booktitle = {Biocomputing 2020},
     pages = {355-366},
     doi = {10.1142/9789811215636_0032}
}
		

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