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<meta name="DC.Title" content="Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas" />
<meta name="DC.Identifier" content="10.3174/ajnr.A6365" />
<meta name="DC.Date" content="2020-01-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.

MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.

RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value &lt; .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035.

CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

C-indices
:   concordance indices
CNN
:   convolutional neural network
GBM
:   glioblastoma multiforme
HGG
:   high-grade glioma
OS
:   overall survival
SE
:   spin-echo
TCGA
:   the Cancer Genome Atlas" />
<meta name="DC.Contributor" content="W. Han" />
<meta name="DC.Contributor" content="L. Qin" />
<meta name="DC.Contributor" content="C. Bay" />
<meta name="DC.Contributor" content="X. Chen" />
<meta name="DC.Contributor" content="K.-H. Yu" />
<meta name="DC.Contributor" content="N. Miskin" />
<meta name="DC.Contributor" content="A. Li" />
<meta name="DC.Contributor" content="X. Xu" />
<meta name="DC.Contributor" content="G. Young" />
<meta name="article:published_time" content="2020-01-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test &lt;i&gt;P&lt;/i&gt; value &amp;lt; .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test &lt;i&gt;P&lt;/i&gt; value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test &lt;i&gt;P&lt;/i&gt; value of .035.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.&lt;/p&gt;" />
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<meta name="twitter:title" content="Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas" />
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<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/41/1.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.

MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.

RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value &lt; .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035.

CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

C-indices
:   concordance indices
CNN
:   convolutional neural network
GBM
:   glioblastoma multiforme
HGG
:   high-grade glioma
OS
:   overall survival
SE
:   spin-echo
TCGA
:   the Cancer Genome Atlas" />
<meta name="og-title" property="og:title" content="Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/41/1/40" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.

MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.

RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test P value &lt; .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035.

CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.

C-indices
:   concordance indices
CNN
:   convolutional neural network
GBM
:   glioblastoma multiforme
HGG
:   high-grade glioma
OS
:   overall survival
SE
:   spin-echo
TCGA
:   the Cancer Genome Atlas" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node72456--5" data-node-nid="72456" id="top-node-72456--6908157528" data-pisa="ajnr;41/1/40" data-pisa-master="ajnr;ajnr.A6365" data-apath="/ajnr/41/1/40.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-jcore-article-title-complete clearfix has-author-tooltip highwire-citation-highwire-article-top-a">
  
  
      <div class="highwire-cite-overline"><span  class="highwire-cite-metadata-article-type highwire-cite-metadata">Research Article</span><span class="separator-pipe"></span><span  class="highwire-cite-metadata-art-cat highwire-cite-metadata"><span class="wrapper">Adult Brain</span></span></div>
  
      <div class="highwire-cite-access"><span class="highwire-citation-access"><i class="highwire-access-icon highwire-access-icon-open-access open-access hw-icon-open-access" title="Open Access" aria-hidden="true"></i><span  class="element-invisible highwire-access-icon highwire-access-icon-open-access" style="">Open Access</span></span></div>
  
      <h1 class="highwire-cite-title" id="page-title">Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">W. Han</span>, <span class="highwire-citation-author" data-delta="1">L. Qin</span>, <span class="highwire-citation-author" data-delta="2">C. Bay</span>, <span class="highwire-citation-author" data-delta="3">X. Chen</span>, <span class="highwire-citation-author" data-delta="4">K.-H. Yu</span>, <span class="highwire-citation-author" data-delta="5">N. Miskin</span>, <span class="highwire-citation-author" data-delta="6">A. Li</span>, <span class="highwire-citation-author" data-delta="7">X. Xu</span> and <span class="highwire-citation-author" data-delta="8">G. Young</span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">January 2020,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">40-48; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6365 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-72456--6908157528" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">W. Han </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=W.%2BHan%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=Han%20W&amp;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%3AW.%2BHan%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 last"><a href="https://orcid.org/0000-0002-0584-3854" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for W. Han</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">L. Qin </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Imaging (L.Q., G.Y.), Dana-Farber Cancer Institute, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=L.%2BQin%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=Qin%20L&amp;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%3AL.%2BQin%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 last"><a href="https://orcid.org/0000-0002-6708-1285" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Qin</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">C. Bay </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=C.%2BBay%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=Bay%20C&amp;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%3AC.%2BBay%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 last"><a href="https://orcid.org/0000-0001-7197-3465" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. Bay</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">X. Chen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Department of Radiology (X.C.), Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=X.%2BChen%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=Chen%20X&amp;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%3AX.%2BChen%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 last"><a href="https://orcid.org/0000-0002-3873-9041" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for X. Chen</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">K.-H. Yu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=K.-H.%2BYu%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=Yu%20K&amp;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%3AK.-H.%2BYu%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 last"><a href="https://orcid.org/0000-0001-9892-8218" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K.-H. Yu</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">N. Miskin </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=N.%2BMiskin%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=Miskin%20N&amp;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%3AN.%2BMiskin%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 last"><a href="https://orcid.org/0000-0002-5651-310X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for N. Miskin</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">A. Li </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=A.%2BLi%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=Li%20A&amp;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%3AA.%2BLi%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 last"><a href="https://orcid.org/0000-0002-2551-3238" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Li</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">X. Xu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=X.%2BXu%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=Xu%20X&amp;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%3AX.%2BXu%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 last"><a href="https://orcid.org/0000-0003-0813-7979" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for X. Xu</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">G. Young </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology (W.H., C.B., X.C., N.M., A.L., X.X., G.Y.), Brigham and Women’s Hospital, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Imaging (L.Q., G.Y.), Dana-Farber Cancer Institute, Boston, Massachusetts</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Harvard Medical School (W.H., L.Q., C.B., K.-H.Y., N.M., X.X., G.Y.), Boston, Massachusetts</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=G.%2BYoung%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=Young%20G&amp;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%3AG.%2BYoung%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 last"><a href="https://orcid.org/0000-0001-8213-865X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for G. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-2"><h2>Abstract</h2><div id="sec-1" class="subsection"><p id="p-3"><strong>BACKGROUND AND PURPOSE:</strong> Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas.</p></div><div id="sec-2" class="subsection"><p id="p-4"><strong>MATERIALS AND METHODS:</strong> Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long- and short-term survivors.</p></div><div id="sec-3" class="subsection"><p id="p-5"><strong>RESULTS:</strong> In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long- and short-term survivor groups with a log-rank test <em>P</em> value &lt; .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long- and short-term survivors with a log-rank test <em>P</em> value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test <em>P</em> value of .035.</p></div><div id="sec-4" class="subsection"><p id="p-6"><strong>CONCLUSIONS:</strong> A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long- and short-term survivors.</p></div></div><div class="section glossary" id="glossary-1"><h2 class="">ABBREVIATIONS:</h2><dl id="def-list-1"><dt id="def-item-1">C-indices<span class="def-item-dt-sep"></span></dt><dd>concordance indices<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">CNN<span class="def-item-dt-sep"></span></dt><dd>convolutional neural network<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">GBM<span class="def-item-dt-sep"></span></dt><dd>glioblastoma multiforme<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">HGG<span class="def-item-dt-sep"></span></dt><dd>high-grade glioma<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">OS<span class="def-item-dt-sep"></span></dt><dd>overall survival<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">SE<span class="def-item-dt-sep"></span></dt><dd>spin-echo<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">TCGA<span class="def-item-dt-sep"></span></dt><dd>the Cancer Genome Atlas</dd></dl></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2020 by American Journal of Neuroradiology</li></ul><div class="license" id="license-1"><p id="p-1">Indicates open access to non-subscribers at <a href="http://www.ajnr.org" rel="license">www.ajnr.org</a></p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/41/1/40.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 41</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 1 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Jan 2020 </div></div>
      
      
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Jan 2020, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">40-48; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6365 </span></div>
  
  
  
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