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’...
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| Veröffentlicht in: | Medical & biological engineering & computing 2020-05, Vol.58 (5), p.1031-1045 |
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| creator | Zadeh Shirazi, Amin Fornaciari, Eric Bagherian, Narjes Sadat Ebert, Lisa M. Koszyca, Barbara Gomez, Guillermo A. |
| description | 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). |
| doi_str_mv | 10.1007/s11517-020-02147-3 |
| format | Article |
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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).</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-020-02147-3</identifier><identifier>PMID: 32124225</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial intelligence ; Artificial neural networks ; Automation ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Brain ; Brain cancer ; Brain Neoplasms - mortality ; Brain Neoplasms - pathology ; Cancer ; Classification ; Classifiers ; Computer Applications ; Datasets ; Deep Learning ; Genomes ; Histocytochemistry ; Human Physiology ; Humans ; Image classification ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Medical imaging ; Mutation ; Neural networks ; Neural Networks, Computer ; Original ; Original Article ; Patients ; Radiology ; Survival ; Survival Analysis</subject><ispartof>Medical & biological engineering & computing, 2020-05, Vol.58 (5), p.1031-1045</ispartof><rights>The Author(s) 2020</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-8265ccfde3a6941cfa7e5a8737ea5b8c1b2923c780253982a1d84c58580b75ce3</citedby><cites>FETCH-LOGICAL-c540t-8265ccfde3a6941cfa7e5a8737ea5b8c1b2923c780253982a1d84c58580b75ce3</cites><orcidid>0000-0002-0494-2404 ; 0000-0002-1906-9900 ; 0000-0002-8041-9666</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-020-02147-3$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-020-02147-3$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32124225$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zadeh Shirazi, Amin</creatorcontrib><creatorcontrib>Fornaciari, Eric</creatorcontrib><creatorcontrib>Bagherian, Narjes Sadat</creatorcontrib><creatorcontrib>Ebert, Lisa M.</creatorcontrib><creatorcontrib>Koszyca, Barbara</creatorcontrib><creatorcontrib>Gomez, Guillermo A.</creatorcontrib><title>DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>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).</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Automation</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Brain</subject><subject>Brain cancer</subject><subject>Brain Neoplasms - mortality</subject><subject>Brain Neoplasms - pathology</subject><subject>Cancer</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Computer Applications</subject><subject>Datasets</subject><subject>Deep Learning</subject><subject>Genomes</subject><subject>Histocytochemistry</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Image 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engineering & computing</jtitle><stitle>Med Biol Eng Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2020-05-01</date><risdate>2020</risdate><volume>58</volume><issue>5</issue><spage>1031</spage><epage>1045</epage><pages>1031-1045</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>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).</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>32124225</pmid><doi>10.1007/s11517-020-02147-3</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-0494-2404</orcidid><orcidid>https://orcid.org/0000-0002-1906-9900</orcidid><orcidid>https://orcid.org/0000-0002-8041-9666</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Artificial intelligence Artificial neural networks Automation Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Brain Brain cancer Brain Neoplasms - mortality Brain Neoplasms - pathology Cancer Classification Classifiers Computer Applications Datasets Deep Learning Genomes Histocytochemistry Human Physiology Humans Image classification Image Interpretation, Computer-Assisted - methods Imaging Medical imaging Mutation Neural networks Neural Networks, Computer Original Original Article Patients Radiology Survival Survival Analysis |
| title | DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images |
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