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<meta name="DC.Title" content="Improved Glioma Grading Using Deep Convolutional Neural Networks" />
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<meta name="DC.Date" content="2021-02-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade prediction. These features may not fully exploit the underlying information in MR images. The objective of this study was to investigate the performance of features learned by a convolutional neural network compared with standard radiomic features for grade prediction.

MATERIALS AND METHODS: A total of 237 patients with gliomas were included in this study. All images were resampled, registered, skull-stripped, and segmented to extract the tumors. The learned features from the trained convolutional neural network were used for grade prediction. The performance of the proposed method was compared with standard machine learning approaches, support vector machine, random forests, and gradient boosting trained with radiomic features.

RESULTS: The experimental results demonstrate that using learned features extracted from the convolutional neural network achieves an average accuracy of 87%, outperforming the methods considering radiomic features alone. The top-performing machine learning model is gradient boosting with an average accuracy of 64%. Thus, there is a 23% improvement in accuracy, and it is an efficient technique for grade prediction.

CONCLUSIONS: Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas. The proposed framework may provide substantial improvement in glioma-grade prediction; however, further validation is needed.

CNN
:   convolutional neural network
GB
:   gradient boosting
ML
:   machine learning
SVM
:   support vector machine
RF
:   random forest
T1CE
:   T1 contrast-enhanced" />
<meta name="DC.Contributor" content="S. Gutta" />
<meta name="DC.Contributor" content="J. Acharya" />
<meta name="DC.Contributor" content="M.S. Shiroishi" />
<meta name="DC.Contributor" content="D. Hwang" />
<meta name="DC.Contributor" content="K.S. Nayak" />
<meta name="article:published_time" content="2021-02-01" />
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<meta name="citation_title" content="Improved Glioma Grading Using Deep Convolutional Neural Networks" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade prediction. These features may not fully exploit the underlying information in MR images. The objective of this study was to investigate the performance of features learned by a convolutional neural network compared with standard radiomic features for grade prediction.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;A total of 237 patients with gliomas were included in this study. All images were resampled, registered, skull-stripped, and segmented to extract the tumors. The learned features from the trained convolutional neural network were used for grade prediction. The performance of the proposed method was compared with standard machine learning approaches, support vector machine, random forests, and gradient boosting trained with radiomic features.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;The experimental results demonstrate that using learned features extracted from the convolutional neural network achieves an average accuracy of 87%, outperforming the methods considering radiomic features alone. The top-performing machine learning model is gradient boosting with an average accuracy of 64%. Thus, there is a 23% improvement in accuracy, and it is an efficient technique for grade prediction.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas. The proposed framework may provide substantial improvement in glioma-grade prediction; however, further validation is needed.&lt;/p&gt;" />
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<meta name="twitter:title" content="Improved Glioma Grading Using Deep Convolutional Neural Networks" />
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<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/42/2.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade prediction. These features may not fully exploit the underlying information in MR images. The objective of this study was to investigate the performance of features learned by a convolutional neural network compared with standard radiomic features for grade prediction.

MATERIALS AND METHODS: A total of 237 patients with gliomas were included in this study. All images were resampled, registered, skull-stripped, and segmented to extract the tumors. The learned features from the trained convolutional neural network were used for grade prediction. The performance of the proposed method was compared with standard machine learning approaches, support vector machine, random forests, and gradient boosting trained with radiomic features.

RESULTS: The experimental results demonstrate that using learned features extracted from the convolutional neural network achieves an average accuracy of 87%, outperforming the methods considering radiomic features alone. The top-performing machine learning model is gradient boosting with an average accuracy of 64%. Thus, there is a 23% improvement in accuracy, and it is an efficient technique for grade prediction.

CONCLUSIONS: Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas. The proposed framework may provide substantial improvement in glioma-grade prediction; however, further validation is needed.

CNN
:   convolutional neural network
GB
:   gradient boosting
ML
:   machine learning
SVM
:   support vector machine
RF
:   random forest
T1CE
:   T1 contrast-enhanced" />
<meta name="og-title" property="og:title" content="Improved Glioma Grading Using Deep Convolutional Neural Networks" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/42/2/233" />
<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: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade prediction. These features may not fully exploit the underlying information in MR images. The objective of this study was to investigate the performance of features learned by a convolutional neural network compared with standard radiomic features for grade prediction.

MATERIALS AND METHODS: A total of 237 patients with gliomas were included in this study. All images were resampled, registered, skull-stripped, and segmented to extract the tumors. The learned features from the trained convolutional neural network were used for grade prediction. The performance of the proposed method was compared with standard machine learning approaches, support vector machine, random forests, and gradient boosting trained with radiomic features.

RESULTS: The experimental results demonstrate that using learned features extracted from the convolutional neural network achieves an average accuracy of 87%, outperforming the methods considering radiomic features alone. The top-performing machine learning model is gradient boosting with an average accuracy of 64%. Thus, there is a 23% improvement in accuracy, and it is an efficient technique for grade prediction.

CONCLUSIONS: Convolutional neural networks are able to learn discriminating features automatically, and these features provide added value for grading gliomas. The proposed framework may provide substantial improvement in glioma-grade prediction; however, further validation is needed.

CNN
:   convolutional neural network
GB
:   gradient boosting
ML
:   machine learning
SVM
:   support vector machine
RF
:   random forest
T1CE
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node88765--5" data-node-nid="88765" id="top-node-88765--61550827878" data-pisa="ajnr;42/2/233" data-pisa-master="ajnr;ajnr.A6882" data-apath="/ajnr/42/2/233.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">Improved Glioma Grading Using Deep Convolutional Neural Networks</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">S. Gutta</span>, <span class="highwire-citation-author" data-delta="1">J. Acharya</span>, <span class="highwire-citation-author" data-delta="2">M.S. Shiroishi</span>, <span class="highwire-citation-author" data-delta="3">D. Hwang</span> and <span class="highwire-citation-author" data-delta="4">K.S. Nayak</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">February 2021,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">42 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">233-239; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6882 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-88765--61550827878" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">S. Gutta </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Ming Hsieh Department of Electrical and Computer Engineering (S.G., K.S.N.), Viterbi School of Engineering</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=S.%2BGutta%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=Gutta%20S&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%3AS.%2BGutta%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/0000-0003-3610-0401" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S. Gutta</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">J. Acharya </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California</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=J.%2BAcharya%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=Acharya%20J&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%3AJ.%2BAcharya%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/0000-0003-0873-0836" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Acharya</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">M.S. Shiroishi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California</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=M.S.%2BShiroishi%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=Shiroishi%20M&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%3AM.S.%2BShiroishi%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/0000-0002-1422-0288" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.S. Shiroishi</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">D. Hwang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California</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=D.%2BHwang%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=Hwang%20D&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%3AD.%2BHwang%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/0000-0002-1111-9176" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for D. Hwang</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">K.S. Nayak </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Ming Hsieh Department of Electrical and Computer Engineering (S.G., K.S.N.), Viterbi School of Engineering</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California</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.S.%2BNayak%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=Nayak%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.S.%2BNayak%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/0000-0001-5735-3550" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K.S. Nayak</a></li></ul></div></div></div>  </div>

  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" xmlns:xhtml="http://www.w3.org/1999/xhtml" class="section ref-list" id="ref-list-1"><h2 class="">References</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a><div class="cit ref-cit ref-journal" id="cit-42.2.233.1" data-doi="10.1016/S0140-6736(18)30990-5"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Lapointe</span>  <span class="cit-name-given-names">S</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Perry</span>  <span class="cit-name-given-names">A</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Butowski</span>  <span class="cit-name-given-names">NA</span></span></li></ol><cite>. <span class="cit-article-title">Primary brain tumours in adults</span>. <abbr 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 42</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 2 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Feb 2021 </div></div>
      
      
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      <div class="highwire-cite-title">Improved Glioma Grading Using Deep Convolutional Neural Networks</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Gutta</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Acharya</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">M.S.</span> <span class="nlm-surname">Shiroishi</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">D.</span> <span class="nlm-surname">Hwang</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">K.S.</span> <span class="nlm-surname">Nayak</span></span></span></div>
  
    	<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">Feb 2021, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">42 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(2) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">233-239; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6882 </span></div>
  
  
  
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