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DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

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dc.contributor.authorZadeh Shirazi, A.
dc.contributor.authorFornaciari, E.
dc.contributor.authorBagherian, N.S.
dc.contributor.authorEbert, L.M.
dc.contributor.authorKoszyca, B.
dc.contributor.authorGomez, G.A.
dc.date.issued2020
dc.descriptionData source: Electronic supplementary material, https://doi.org/10.1007/s11517-020-02147-3
dc.description.abstractHistopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer. Graphical abstract A DCNN model was generated to accurately predict survival rates of brain cancer patients (classified in 4 different classes) accurately. After training the model using images from H&E stained tissue biopsies from The Cancer Genome Atlas database (TCGA, left), the model can predict for each patient, based on a histological image (top right), its survival class accurately (bottom right).
dc.description.statementofresponsibilityAmin Zadeh Shirazi, Eric Fornaciari, Narjes Sadat Bagherian, Lisa M. Ebert, Barbara Koszyca, Guillermo A. Gomez
dc.identifier.citationMedical and Biological Engineering and Computing, 2020; 58(5):1031-1045
dc.identifier.doi10.1007/s11517-020-02147-3
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.orcidEbert, L.M. [0000-0002-8041-9666]
dc.identifier.urihttp://hdl.handle.net/2440/124682
dc.language.isoen
dc.publisherSpringer
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1067405
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1123816
dc.relation.granthttp://purl.org/au-research/grants/arc/FT160100366
dc.rights© The Author(s) 2020
dc.source.urihttps://doi.org/10.1007/s11517-020-02147-3
dc.subjectBrain cancer
dc.subjectClassification
dc.subjectConvolutional neural networks
dc.subjectDeep learning
dc.subjectHistopathological images
dc.subjectSurvival rate
dc.titleDeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images
dc.typeJournal article
pubs.publication-statusPublished

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