DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.
Histopathological 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' surv...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 5; pp. 1031 - 1046 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142943692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142943692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2020 vid: 58 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142943692 142943692 NLM32124225 10.1007/s11517-020-02147-3 NLM32124225 142943692 ppf: 1031 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images. aug: au: Zadeh Shirazi, Amin Fornaciari, Eric Bagherian, Narjes Sadat Ebert, Lisa M. Koszyca, Barbara Gomez, Guillermo A. affil: Centre for Cancer Biology, SA Pathology and University of South Australia, UniSA CRI Building, North Terrace, 5001, Adelaide, SA, Australia sug: ab: Histopathological 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). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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