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This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available. We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans. This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification." /> <meta name="DC.Contributor" content="Jakob Nikolas Kather" /> <meta name="DC.Contributor" content="Jefree Schulte" /> <meta name="DC.Contributor" content="Heike I. Grabsch" /> <meta name="DC.Contributor" content="Chiara Loeffler" /> <meta name="DC.Contributor" content="Hannah Muti" /> <meta name="DC.Contributor" content="James Dolezal" /> <meta name="DC.Contributor" content="Andrew Srisuwananukorn" /> <meta name="DC.Contributor" content="Nishant Agrawal" /> <meta name="DC.Contributor" content="Sara Kochanny" /> <meta name="DC.Contributor" content="Saskia von Stillfried" /> <meta name="DC.Contributor" content="Peter Boor" /> <meta name="DC.Contributor" content="Takaki Yoshikawa" /> <meta name="DC.Contributor" content="Dirk Jaeger" /> <meta name="DC.Contributor" content="Christian Trautwein" /> <meta name="DC.Contributor" content="Peter Bankhead" /> <meta name="DC.Contributor" content="Nicole A. Cipriani" /> <meta name="DC.Contributor" content="Tom Luedde" /> <meta name="DC.Contributor" content="Alexander T. Pearson" /> <meta name="article:published_time" content="2019-07-05" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Deep learning detects virus presence in cancer histology" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.</p><p>We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.</p><p>This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2019/01/01" /> <meta name="citation_mjid" content="biorxiv;690206v1" /> <meta name="citation_id" content="690206v1" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/690206v1" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/690206v1.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/690206v1.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2019/07/05/690206.full.pdf" /> <meta name="citation_doi" content="10.1101/690206" /> <meta name="citation_num_pages" content="19" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="690206" /> <meta name="citation_author" content="Jakob Nikolas Kather" /> <meta name="citation_author_institution" content="Department of Medicine III, University Hospital RWTH Aachen" /> <meta name="citation_author_institution" content="German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ)" /> <meta name="citation_author_institution" content="Applied Tumor Immunity, German Cancer Research Center (DKFZ)" /> <meta name="citation_author_institution" content="Department of Medicine, University of Chicago" /> <meta name="citation_author_email" content="jakob.kather@gmail.com" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0002-3730-5348" /> <meta name="citation_author" content="Jefree Schulte" /> <meta name="citation_author_institution" content="Department of Pathology, University of Chicago Medicine" /> <meta name="citation_author" content="Heike I. Grabsch" /> <meta name="citation_author_institution" content="Pathology & Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds" /> <meta name="citation_author_institution" content="Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+" /> <meta name="citation_author" content="Chiara Loeffler" /> <meta name="citation_author_institution" content="Department of Medicine III, University Hospital RWTH Aachen" /> <meta name="citation_author" content="Hannah Muti" /> <meta name="citation_author_institution" content="Department of Medicine III, University Hospital RWTH Aachen" /> <meta name="citation_author" content="James Dolezal" /> <meta name="citation_author_institution" content="Department of Medicine, University of Chicago" /> <meta name="citation_author" content="Andrew Srisuwananukorn" /> <meta name="citation_author_institution" content="Department of Medicine, University of Illinois – Chicago" /> <meta name="citation_author" content="Nishant Agrawal" /> <meta name="citation_author_institution" content="Department of Surgery, University of Chicago" /> <meta name="citation_author" content="Sara Kochanny" /> <meta name="citation_author_institution" content="Department of Medicine, University of Chicago" /> <meta name="citation_author" content="Saskia von Stillfried" /> <meta name="citation_author_institution" content="Department of Pathology, University Hospital RWTH Aachen" /> <meta name="citation_author" content="Peter Boor" /> <meta name="citation_author_institution" content="Department of Pathology, University Hospital RWTH Aachen" /> <meta name="citation_author" content="Takaki Yoshikawa" /> <meta name="citation_author_institution" content="Department of Gastrointestinal Surgery, Kanagawa Cancer Center" /> <meta name="citation_author_institution" content="Department of Gastric Surgery, National Cancer Center Hospital" /> <meta name="citation_author" content="Dirk Jaeger" /> <meta name="citation_author_institution" content="German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ)" /> <meta name="citation_author_institution" content="Applied Tumor Immunity, German Cancer Research Center (DKFZ)" /> <meta name="citation_author" content="Christian Trautwein" /> <meta name="citation_author_institution" content="Department of Medicine III, University Hospital RWTH Aachen" /> <meta name="citation_author" content="Peter Bankhead" /> <meta name="citation_author_institution" content="MRC Institute of Genetics and Molecular Medicine, University of Edinburgh" /> <meta name="citation_author" content="Nicole A. Cipriani" /> <meta name="citation_author_institution" content="Department of Pathology, University of Chicago Medicine" /> <meta name="citation_author" content="Tom Luedde" /> <meta name="citation_author_institution" content="Department of Medicine III, University Hospital RWTH Aachen" /> <meta name="citation_author" content="Alexander T. Pearson" /> <meta name="citation_author_institution" content="Department of Medicine, University of Chicago" /> <meta name="citation_reference" content="Bansal, A., Singh, M.P. & Rai, B. Human papillomavirus-associated cancers: A growing global problem. Int J Appl Basic Med Res 6, 84–89 (2016)." /> <meta name="citation_reference" content="Mesri, E.A., Feitelson, M.A. & Munger, K. Human viral oncogenesis: a cancer hallmarks analysis. Cell Host Microbe 15, 266–282 (2014)." /> <meta name="citation_reference" content="Chaturvedi, A.K., et al. Human papillomavirus and rising oropharyngeal cancer incidence in the United States. 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However, molecular tests for HPV and EBV are not ubiquitously available. We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans. This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification." /> <meta name="og-title" property="og:title" content="Deep learning detects virus presence in cancer histology" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/690206v1" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available. We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans. This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="citation_date" content="2019-07-05" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/690206v1.ppt" /> <meta name="description" content="bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution" /> <meta name="generator" content="Drupal 7 (http://drupal.org)" /> <link rel="canonical" href="https://www.biorxiv.org/content/10.1101/690206v1" /> <link rel="shortlink" href="https://www.biorxiv.org/node/806980" /> <title>Deep learning detects virus presence in 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data-apath="/biorxiv/early/2019/07/05/690206.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-biorxiv-article-top clearfix has-author-tooltip" > <span class="biorxiv-article-type"> New Results </span> <h1 class="highwire-cite-title" id="page-title">Deep learning detects virus presence in cancer histology</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first hw-author-orcid-logo-wrapper" data-delta="0"><a href="http://orcid.org/0000-0002-3730-5348" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Jakob Nikolas</span> <span class="nlm-surname">Kather</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Jefree</span> <span class="nlm-surname">Schulte</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Heike I.</span> <span class="nlm-surname">Grabsch</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Chiara</span> <span class="nlm-surname">Loeffler</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Hannah</span> <span class="nlm-surname">Muti</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">James</span> <span class="nlm-surname">Dolezal</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Andrew</span> <span class="nlm-surname">Srisuwananukorn</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Nishant</span> <span class="nlm-surname">Agrawal</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Sara</span> <span class="nlm-surname">Kochanny</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Saskia von</span> <span class="nlm-surname">Stillfried</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Peter</span> <span class="nlm-surname">Boor</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Takaki</span> <span class="nlm-surname">Yoshikawa</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">Dirk</span> <span class="nlm-surname">Jaeger</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Christian</span> <span class="nlm-surname">Trautwein</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">Peter</span> <span class="nlm-surname">Bankhead</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">Nicole A.</span> <span class="nlm-surname">Cipriani</span></span>, <span class="highwire-citation-author" data-delta="16"><span class="nlm-given-names">Tom</span> <span class="nlm-surname">Luedde</span></span>, <span class="highwire-citation-author" data-delta="17"><span class="nlm-given-names">Alexander T.</span> <span class="nlm-surname">Pearson</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/690206 </span></div> </div> <div id="hw-article-author-popups-node-806980--21064937480" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Jakob Nikolas Kather </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Medicine III, University Hospital RWTH Aachen</span>, Aachen, <span class='nlm-country'>Germany</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ)</span>, Heidelberg, <span class='nlm-country'>Germany</span></div><div class='author-affiliation'><span class='nlm-sup'>3</span><span class='nlm-institution'>Applied Tumor Immunity, German Cancer Research Center (DKFZ)</span>, Heidelberg, <span class='nlm-country'>Germany</span></div><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>Department of Medicine, University of Chicago</span>, Chicago, IL, <span class='nlm-country'>USA</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jakob%2BNikolas%2BKather%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Kather%20JN&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AJakob%2BNikolas%2BKather%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link"><a href="http://orcid.org/0000-0002-3730-5348" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Jakob Nikolas Kather</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:jakob.kather@gmail.com" class="" data-icon-position="" 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class='nlm-sup'>8</span><span class='nlm-institution'>Department of Medicine, University of Illinois – Chicago</span>, Chicago, IL, <span class='nlm-country'>USA</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Andrew%2BSrisuwananukorn%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Srisuwananukorn%20A&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AAndrew%2BSrisuwananukorn%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">Nishant Agrawal </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>9</span><span class='nlm-institution'>Department of Surgery, University of Chicago</span>, Chicago, IL, <span class='nlm-country'>USA</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Nishant%2BAgrawal%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Agrawal%20N&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ANishant%2BAgrawal%2B" rel="nofollow" class="" data-icon-position="" 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class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">Abstract</h2><p id="p-2">Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.</p><p id="p-3">We hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.</p><p id="p-4">This workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.</p></div><div class="section fn-group" id="fn-group-1"><h2>Footnotes</h2><ul><li class="fn-dataset fn-group-external-links" id="fn-1"><p id="p-5"> <a href="https://dx.doi.org/10.5281/zenodo.3247009">https://dx.doi.org/10.5281/zenodo.3247009</a> </p></li><li class="fn-dataset fn-group-external-links" id="fn-2"><p id="p-6"> <a href="https://portal.gdc.cancer.gov/">https://portal.gdc.cancer.gov/</a> </p></li><li class="fn-dataset fn-group-external-links" id="fn-3"><p id="p-7"> <a href="https://github.com/jnkather/VirusFromHE">https://github.com/jnkather/VirusFromHE</a> </p></li></ul></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" 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