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<meta name="DC.Language" content="en" />
<meta name="DC.Title" content="Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging" />
<meta name="DC.Identifier" content="10.3174/ajnr.A6621" />
<meta name="DC.Date" content="2020-07-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="© 2020 by American Journal of Neuroradiology. Indicates open access to non-subscribers at www.ajnr.org" />
<meta name="DC.AccessRights" content="open-access" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.

MATERIALS AND METHODS: This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma ( n  = 111), ependymoma ( n  = 70), and pilocytic astrocytoma ( n  = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.

RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P  &lt; .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.

CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

AUC
:   area under the curve
AutoML
:   automatic machine learning
CHSQ
:   χ2 score
EP
:   ependymoma
MB
:   medulloblastoma
ML
:   machine learning
PA
:   pilocytic astrocytoma
TPOT
:   Tree-Based Pipeline Optimization Tool" />
<meta name="DC.Contributor" content="H. Zhou" />
<meta name="DC.Contributor" content="R. Hu" />
<meta name="DC.Contributor" content="O. Tang" />
<meta name="DC.Contributor" content="C. Hu" />
<meta name="DC.Contributor" content="L. Tang" />
<meta name="DC.Contributor" content="K. Chang" />
<meta name="DC.Contributor" content="Q. Shen" />
<meta name="DC.Contributor" content="J. Wu" />
<meta name="DC.Contributor" content="B. Zou" />
<meta name="DC.Contributor" content="B. Xiao" />
<meta name="DC.Contributor" content="J. Boxerman" />
<meta name="DC.Contributor" content="W. Chen" />
<meta name="DC.Contributor" content="R.Y. Huang" />
<meta name="DC.Contributor" content="L. Yang" />
<meta name="DC.Contributor" content="H.X. Bai" />
<meta name="DC.Contributor" content="C. Zhu" />
<meta name="article:published_time" content="2020-07-01" />
<meta name="article:section" content="Pediatrics" />
<meta name="citation_title" content="Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (&lt;i&gt;n&lt;/i&gt; = 111), ependymoma (&lt;i&gt;n&lt;/i&gt; = 70), and pilocytic astrocytoma (&lt;i&gt;n&lt;/i&gt; = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ&lt;sup&gt;2&lt;/sup&gt; score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, &lt;i&gt;P &lt;/i&gt;&amp;lt; .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2020/07/01" />
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<meta name="citation_issn" content="0195-6108" />
<meta name="citation_issn" content="1936-959X" />
<meta name="citation_doi" content="10.3174/ajnr.A6621" />
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<meta name="citation_volume" content="41" />
<meta name="citation_issue" content="7" />
<meta name="citation_article_type" content="Research Article" />
<meta name="citation_section" content="Pediatrics" />
<meta name="citation_firstpage" content="1279" />
<meta name="citation_lastpage" content="1285" />
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<meta name="citation_author" content="H. Zhou" />
<meta name="citation_author_institution" content="gDepartment of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China" />
<meta name="citation_author_orcid" content="https://orcid.org/0000-0001-5648-0574" />
<meta name="citation_author" content="R. Hu" />
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<meta name="twitter:title" content="Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.

MATERIALS AND METHODS: This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma ( n  = 111), ependymoma ( n  = 70), and pilocytic astrocytoma ( n  = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.

RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P  &lt; .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.

CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

AUC
:   area under the curve
AutoML
:   automatic machine learning
CHSQ
:   χ2 score
EP
:   ependymoma
MB
:   medulloblastoma
ML
:   machine learning
PA
:   pilocytic astrocytoma
TPOT
:   Tree-Based Pipeline Optimization Tool" />
<meta name="og-title" property="og:title" content="Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/41/7/1279" />
<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: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.

MATERIALS AND METHODS: This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma ( n  = 111), ependymoma ( n  = 70), and pilocytic astrocytoma ( n  = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation.

RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ2 score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P  &lt; .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma.

CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

AUC
:   area under the curve
AutoML
:   automatic machine learning
CHSQ
:   χ2 score
EP
:   ependymoma
MB
:   medulloblastoma
ML
:   machine learning
PA
:   pilocytic astrocytoma
TPOT
:   Tree-Based Pipeline Optimization Tool" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node76573--5" data-node-nid="76573" id="top-node-76573--61981696677" data-pisa="ajnr;41/7/1279" data-pisa-master="ajnr;41/7/1279" data-apath="/ajnr/41/7/1279.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">Pediatrics</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">Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">H. Zhou</span>, <span class="highwire-citation-author" data-delta="1">R. Hu</span>, <span class="highwire-citation-author" data-delta="2">O. Tang</span>, <span class="highwire-citation-author" data-delta="3">C. Hu</span>, <span class="highwire-citation-author" data-delta="4">L. Tang</span>, <span class="highwire-citation-author" data-delta="5">K. Chang</span>, <span class="highwire-citation-author" data-delta="6">Q. Shen</span>, <span class="highwire-citation-author" data-delta="7">J. Wu</span>, <span class="highwire-citation-author" data-delta="8">B. Zou</span>, <span class="highwire-citation-author" data-delta="9">B. Xiao</span>, <span class="highwire-citation-author" data-delta="10">J. Boxerman</span>, <span class="highwire-citation-author" data-delta="11">W. Chen</span>, <span class="highwire-citation-author" data-delta="12">R.Y. Huang</span>, <span class="highwire-citation-author" data-delta="13">L. Yang</span>, <span class="highwire-citation-author" data-delta="14">H.X. Bai</span> and <span class="highwire-citation-author" data-delta="15">C. Zhu</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">July 2020,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(7) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1279-1285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6621 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-76573--61981696677" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">H. Zhou </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, 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=H.%2BZhou%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=Zhou%20H&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%3AH.%2BZhou%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/0000-0001-5648-0574" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H. Zhou</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">R. Hu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)</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=R.%2BHu%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=Hu%20R&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%3AR.%2BHu%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/0000-0002-0137-4237" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R. Hu</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">O. Tang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>f</span>Warren Alpert Medical School, Brown University (O.T.), Providence, Rhode Island</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=O.%2BTang%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=Tang%20O&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%3AO.%2BTang%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/0000-0002-8604-2708" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for O. Tang</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">C. Hu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>j</span>Department of Neurology (C.H.), Hunan Provincial People’s Hospital, Changsha, Hunan, 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=C.%2BHu%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=Hu%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.%2BHu%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/0000-0002-4937-3444" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. Hu</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">L. Tang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, 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=L.%2BTang%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=Tang%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.%2BTang%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/0000-0002-6823-4810" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Tang</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">K. Chang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>i</span>Department of Radiology (K.C.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 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.%2BChang%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=Chang%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.%2BChang%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/0000-0001-6956-5059" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for K. Chang</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">Q. Shen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, 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=Q.%2BShen%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=Shen%20Q&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%3AQ.%2BShen%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/0000-0002-2837-5299" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Q. Shen</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">J. Wu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, 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=J.%2BWu%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=Wu%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.%2BWu%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/0000-0001-6983-4853" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Wu</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">B. Zou </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)</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=B.%2BZou%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=Zou%20B&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%3AB.%2BZou%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/0000-0002-3353-0740" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B. Zou</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">B. Xiao </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, 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=B.%2BXiao%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=Xiao%20B&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%3AB.%2BXiao%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/0000-0001-5204-1902" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for B. Xiao</a></li></ul></div><div class="author-tooltip-10"><div class="author-tooltip-name">J. Boxerman </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital</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.%2BBoxerman%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=Boxerman%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.%2BBoxerman%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/0000-0002-0594-1542" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Boxerman</a></li></ul></div><div class="author-tooltip-11"><div class="author-tooltip-name">W. Chen </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>k</span>Department of Pathology (W.C.), Hunan Children’s Hospital, Changsha, Hunan, 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=W.%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%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.%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/0000-0001-7570-6014" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for W. Chen</a></li></ul></div><div class="author-tooltip-12"><div class="author-tooltip-name">R.Y. Huang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>h</span>Department of Radiology (R.Y.H.), 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=R.Y.%2BHuang%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=Huang%20R&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%3AR.Y.%2BHuang%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/0000-0001-7661-797X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R.Y. 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Yang </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Departments of Neurology (L.Y.)</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.%2BYang%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=Yang%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.%2BYang%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/0000-0001-5973-3653" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L. Yang</a></li></ul></div><div class="author-tooltip-14"><div class="author-tooltip-name">H.X. Bai </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital</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=H.X.%2BBai%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=Bai%20H&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%3AH.X.%2BBai%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/0000-0002-7460-8866" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H.X. Bai</a></li></ul></div><div class="author-tooltip-15"><div class="author-tooltip-name">C. Zhu </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the School of Computer Science and Engineering (R.H., B.Z., C.Z.)</div><div class='author-affiliation'><span class='nlm-sup'>b</span>College of Literature and Journalism (C.Z.), Central South University, Changsha, Hunan, China</div><div class='author-affiliation'><span class='nlm-sup'>l</span>Mobile Health Ministry of Education-China Mobile Joint Laboratory (C.Z.), 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=C.%2BZhu%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=Zhu%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.%2BZhu%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/0000-0002-5415-7121" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for C. 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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">H.</span> <span class="nlm-surname">Zhou</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">O.</span> <span class="nlm-surname">Tang</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Tang</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Chang</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Q.</span> <span class="nlm-surname">Shen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Wu</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Zou</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Xiao</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Boxerman</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">W.</span> <span class="nlm-surname">Chen</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">R.Y.</span> <span class="nlm-surname">Huang</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Yang</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">H.X.</span> <span class="nlm-surname">Bai</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Zhu</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">Jul 2020, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(7) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1279-1285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6621 </span></div>
  
  
  
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      <div class="highwire-cite-title">Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging</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">H.</span> <span class="nlm-surname">Zhou</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">O.</span> <span class="nlm-surname">Tang</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Hu</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Tang</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">K.</span> <span class="nlm-surname">Chang</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Q.</span> <span class="nlm-surname">Shen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Wu</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Zou</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">B.</span> <span class="nlm-surname">Xiao</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Boxerman</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">W.</span> <span class="nlm-surname">Chen</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">R.Y.</span> <span class="nlm-surname">Huang</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">L.</span> <span class="nlm-surname">Yang</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">H.X.</span> <span class="nlm-surname">Bai</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">C.</span> <span class="nlm-surname">Zhu</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">Jul 2020, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">41 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(7) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1279-1285; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6621 </span></div>
  
  
  
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