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<meta name="DC.Title" content="Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction" />
<meta name="DC.Identifier" content="10.3174/ajnr.A8000" />
<meta name="DC.Date" content="2023-10-01" />
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<meta name="DC.Description" content="BACKGROUND: The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.

PURPOSE: We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.

DATA SOURCES: Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.

STUDY SELECTION: Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.

DATA ANALYSIS: We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

DATA SYNTHESIS: Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.

LIMITATIONS: The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.

CONCLUSIONS: While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

AUC
:   area under the curve
DL
:   deep learning
MGMT 
:   O6-methylguanine-DNA methyltransferase 
ML
:   machine learning
SVM
:   support vector machine
WHO
:   World Health Organization
IDH 
:   isocitrate dehydrogenase" />
<meta name="DC.Contributor" content="Jan Lost" />
<meta name="DC.Contributor" content="Tej Verma" />
<meta name="DC.Contributor" content="Leon Jekel" />
<meta name="DC.Contributor" content="Marc von Reppert" />
<meta name="DC.Contributor" content="Niklas Tillmanns" />
<meta name="DC.Contributor" content="Sara Merkaj" />
<meta name="DC.Contributor" content="Gabriel Cassinelli Petersen" />
<meta name="DC.Contributor" content="Ryan Bahar" />
<meta name="DC.Contributor" content="Ayyüce Gordem" />
<meta name="DC.Contributor" content="Muhammad A. Haider" />
<meta name="DC.Contributor" content="Harry Subramanian" />
<meta name="DC.Contributor" content="Waverly Brim" />
<meta name="DC.Contributor" content="Ichiro Ikuta" />
<meta name="DC.Contributor" content="Antonio Omuro" />
<meta name="DC.Contributor" content="Gian Marco Conte" />
<meta name="DC.Contributor" content="Bernadette V. Marquez-Nostra" />
<meta name="DC.Contributor" content="Arman Avesta" />
<meta name="DC.Contributor" content="Khaled Bousabarah" />
<meta name="DC.Contributor" content="Ali Nabavizadeh" />
<meta name="DC.Contributor" content="Anahita Fathi Kazerooni" />
<meta name="DC.Contributor" content="Sanjay Aneja" />
<meta name="DC.Contributor" content="Spyridon Bakas" />
<meta name="DC.Contributor" content="MingDe Lin" />
<meta name="DC.Contributor" content="Michael Sabel" />
<meta name="DC.Contributor" content="Mariam Aboian" />
<meta name="article:published_time" content="2023-10-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND:&lt;/h3&gt;
&lt;p&gt;The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.&lt;/p&gt;&lt;h3&gt;PURPOSE:&lt;/h3&gt;
&lt;p&gt;We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.&lt;/p&gt;&lt;h3&gt;DATA SOURCES:&lt;/h3&gt;
&lt;p&gt;Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.&lt;/p&gt;&lt;h3&gt;STUDY SELECTION:&lt;/h3&gt;
&lt;p&gt;Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.&lt;/p&gt;&lt;h3&gt;DATA ANALYSIS:&lt;/h3&gt;
&lt;p&gt;We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.&lt;/p&gt;&lt;h3&gt;DATA SYNTHESIS:&lt;/h3&gt;
&lt;p&gt;Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of &lt;i&gt;O6-methylguanine-DNA methyltransferase&lt;/i&gt; promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.&lt;/p&gt;&lt;h3&gt;LIMITATIONS:&lt;/h3&gt;
&lt;p&gt;The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.&lt;/p&gt;" />
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<meta name="citation_author_institution" content="bDepartment of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany" />
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<meta name="citation_author" content="Tej Verma" />
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<meta name="citation_reference" content="RSNA-MICCAI Brain Tumor Radiogenomic Classification. July 13, 2021 to October 15, 2021. https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification. Accessed June 27, 2022" />
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<meta name="twitter:title" content="Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction" />
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<meta name="twitter:description" content="BACKGROUND: The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.

PURPOSE: We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.

DATA SOURCES: Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.

STUDY SELECTION: Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.

DATA ANALYSIS: We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

DATA SYNTHESIS: Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.

LIMITATIONS: The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.

CONCLUSIONS: While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

AUC
:   area under the curve
DL
:   deep learning
MGMT 
:   O6-methylguanine-DNA methyltransferase 
ML
:   machine learning
SVM
:   support vector machine
WHO
:   World Health Organization
IDH 
:   isocitrate dehydrogenase" />
<meta name="og-title" property="og:title" content="Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/44/10/1126" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND: The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor.

PURPOSE: We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging.

DATA SOURCES: Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science.

STUDY SELECTION: Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria.

DATA ANALYSIS: We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines.

DATA SYNTHESIS: Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles.

LIMITATIONS: The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias.

CONCLUSIONS: While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.

AUC
:   area under the curve
DL
:   deep learning
MGMT 
:   O6-methylguanine-DNA methyltransferase 
ML
:   machine learning
SVM
:   support vector machine
WHO
:   World Health Organization
IDH 
:   isocitrate dehydrogenase" />
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      <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>
  
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      <h1 class="highwire-cite-title" id="page-title">Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Jan Lost</span>, <span class="highwire-citation-author" data-delta="1">Tej Verma</span>, <span class="highwire-citation-author" data-delta="2">Leon Jekel</span>, <span class="highwire-citation-author" data-delta="3">Marc von Reppert</span>, <span class="highwire-citation-author" data-delta="4">Niklas Tillmanns</span>, <span class="highwire-citation-author" data-delta="5">Sara Merkaj</span>, <span class="highwire-citation-author" data-delta="6">Gabriel Cassinelli Petersen</span>, <span class="highwire-citation-author" data-delta="7">Ryan Bahar</span>, <span class="highwire-citation-author" data-delta="8">Ayyüce Gordem</span>, <span class="highwire-citation-author" data-delta="9">Muhammad A. Haider</span>, <span class="highwire-citation-author" data-delta="10">Harry Subramanian</span>, <span class="highwire-citation-author" data-delta="11">Waverly Brim</span>, <span class="highwire-citation-author" data-delta="12">Ichiro Ikuta</span>, <span class="highwire-citation-author" data-delta="13">Antonio Omuro</span>, <span class="highwire-citation-author" data-delta="14">Gian Marco Conte</span>, <span class="highwire-citation-author" data-delta="15">Bernadette V. Marquez-Nostra</span>, <span class="highwire-citation-author" data-delta="16">Arman Avesta</span>, <span class="highwire-citation-author" data-delta="17">Khaled Bousabarah</span>, <span class="highwire-citation-author" data-delta="18">Ali Nabavizadeh</span>, <span class="highwire-citation-author" data-delta="19">Anahita Fathi Kazerooni</span>, <span class="highwire-citation-author" data-delta="20">Sanjay Aneja</span>, <span class="highwire-citation-author" data-delta="21">Spyridon Bakas</span>, <span class="highwire-citation-author" data-delta="22">MingDe Lin</span>, <span class="highwire-citation-author" data-delta="23">Michael Sabel</span> and <span class="highwire-citation-author" data-delta="24">Mariam Aboian</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">October 2023,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1126-1134; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A8000 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-104254--61514311124" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Jan Lost </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</div><div class='author-affiliation'><span class='nlm-sup'>b</span>Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany</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=Jan%2BLost%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=Lost%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%3AJan%2BLost%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/0000-0001-6098-6746" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Jan Lost</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Tej Verma </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Tej%2BVerma%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=Verma%20T&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%3ATej%2BVerma%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/0000-0001-9954-3328" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Tej Verma</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Leon Jekel </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Leon%2BJekel%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=Jekel%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 last"><a href="/search/author1%3ALeon%2BJekel%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Marc von Reppert </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Marc%2Bvon%2BReppert%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li 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B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Niklas%2BTillmanns%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=Tillmanns%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%3ANiklas%2BTillmanns%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/0000-0002-9366-7183" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID 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class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Ryan%2BBahar%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=Bahar%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%3ARyan%2BBahar%2B" rel="nofollow" class="" 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Haider </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Muhammad%2BA.%2BHaider%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=Haider%20MA&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 last"><a 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class='nlm-sup'>c</span>Department of Radiology (I.I.), Mayo Clinic Arizona, Phoenix, Arizona</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=Ichiro%2BIkuta%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=Ikuta%20I&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%3AIchiro%2BIkuta%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/0000-0002-7145-833X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID 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Marquez-Nostra </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 and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut</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=Bernadette%2BV.%2BMarquez-Nostra%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=Marquez-Nostra%20BV&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 last"><a 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href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=Khaled%2BBousabarah%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=Bousabarah%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 last"><a href="/search/author1%3AKhaled%2BBousabarah%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-18"><div class="author-tooltip-name">Ali Nabavizadeh </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Department of Radiology (A.N.), Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania</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=Ali%2BNabavizadeh%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=Nabavizadeh%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 last"><a href="/search/author1%3AAli%2BNabavizadeh%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-19"><div class="author-tooltip-name">Anahita Fathi Kazerooni </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>h</span>Department of Neurosurgery (A.F.K.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania</div><div class='author-affiliation'><span class='nlm-sup'>i</span>Division of Neurosurgery (A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania</div><div class='author-affiliation'><span class='nlm-sup'>j</span>Center for Data-Driven Discovery (A.F.K.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania</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=Anahita%2BFathi%2BKazerooni%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=Kazerooni%20AF&amp;link_type=AUTHORSEARCH" target="_blank" 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 44</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 10 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Oct 2023 </div></div>
      
      
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      <div class="highwire-cite-title">Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction</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">Jan</span> <span class="nlm-surname">Lost</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Tej</span> <span class="nlm-surname">Verma</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Leon</span> <span class="nlm-surname">Jekel</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Marc</span> <span class="nlm-surname">von Reppert</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Niklas</span> <span class="nlm-surname">Tillmanns</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Sara</span> <span class="nlm-surname">Merkaj</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Gabriel Cassinelli</span> <span class="nlm-surname">Petersen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Ryan</span> <span class="nlm-surname">Bahar</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Ayyüce</span> <span class="nlm-surname">Gordem</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Muhammad A.</span> <span class="nlm-surname">Haider</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Harry</span> <span class="nlm-surname">Subramanian</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Waverly</span> <span class="nlm-surname">Brim</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">Ichiro</span> <span class="nlm-surname">Ikuta</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Antonio</span> <span class="nlm-surname">Omuro</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">Gian Marco</span> <span class="nlm-surname">Conte</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">Bernadette V.</span> <span class="nlm-surname">Marquez-Nostra</span></span>, <span class="highwire-citation-author" data-delta="16"><span class="nlm-given-names">Arman</span> <span class="nlm-surname">Avesta</span></span>, <span class="highwire-citation-author" data-delta="17"><span class="nlm-given-names">Khaled</span> <span class="nlm-surname">Bousabarah</span></span>, <span class="highwire-citation-author" data-delta="18"><span class="nlm-given-names">Ali</span> <span class="nlm-surname">Nabavizadeh</span></span>, <span class="highwire-citation-author" data-delta="19"><span class="nlm-given-names">Anahita Fathi</span> <span class="nlm-surname">Kazerooni</span></span>, <span class="highwire-citation-author" data-delta="20"><span class="nlm-given-names">Sanjay</span> <span class="nlm-surname">Aneja</span></span>, <span class="highwire-citation-author" data-delta="21"><span class="nlm-given-names">Spyridon</span> <span class="nlm-surname">Bakas</span></span>, <span class="highwire-citation-author" data-delta="22"><span class="nlm-given-names">MingDe</span> <span class="nlm-surname">Lin</span></span>, <span class="highwire-citation-author" data-delta="23"><span class="nlm-given-names">Michael</span> <span class="nlm-surname">Sabel</span></span>, <span class="highwire-citation-author" data-delta="24"><span class="nlm-given-names">Mariam</span> <span class="nlm-surname">Aboian</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">Oct 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1126-1134; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8000 </span></div>
  
  
  
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      <div class="highwire-cite-title">Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction</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">Jan</span> <span class="nlm-surname">Lost</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Tej</span> <span class="nlm-surname">Verma</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">Leon</span> <span class="nlm-surname">Jekel</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">Marc</span> <span class="nlm-surname">von Reppert</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">Niklas</span> <span class="nlm-surname">Tillmanns</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">Sara</span> <span class="nlm-surname">Merkaj</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">Gabriel Cassinelli</span> <span class="nlm-surname">Petersen</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">Ryan</span> <span class="nlm-surname">Bahar</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">Ayyüce</span> <span class="nlm-surname">Gordem</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">Muhammad A.</span> <span class="nlm-surname">Haider</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">Harry</span> <span class="nlm-surname">Subramanian</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">Waverly</span> <span class="nlm-surname">Brim</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">Ichiro</span> <span class="nlm-surname">Ikuta</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">Antonio</span> <span class="nlm-surname">Omuro</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">Gian Marco</span> <span class="nlm-surname">Conte</span></span>, <span class="highwire-citation-author" data-delta="15"><span class="nlm-given-names">Bernadette V.</span> <span class="nlm-surname">Marquez-Nostra</span></span>, <span class="highwire-citation-author" data-delta="16"><span class="nlm-given-names">Arman</span> <span class="nlm-surname">Avesta</span></span>, <span class="highwire-citation-author" data-delta="17"><span class="nlm-given-names">Khaled</span> <span class="nlm-surname">Bousabarah</span></span>, <span class="highwire-citation-author" data-delta="18"><span class="nlm-given-names">Ali</span> <span class="nlm-surname">Nabavizadeh</span></span>, <span class="highwire-citation-author" data-delta="19"><span class="nlm-given-names">Anahita Fathi</span> <span class="nlm-surname">Kazerooni</span></span>, <span class="highwire-citation-author" data-delta="20"><span class="nlm-given-names">Sanjay</span> <span class="nlm-surname">Aneja</span></span>, <span class="highwire-citation-author" data-delta="21"><span class="nlm-given-names">Spyridon</span> <span class="nlm-surname">Bakas</span></span>, <span class="highwire-citation-author" data-delta="22"><span class="nlm-given-names">MingDe</span> <span class="nlm-surname">Lin</span></span>, <span class="highwire-citation-author" data-delta="23"><span class="nlm-given-names">Michael</span> <span class="nlm-surname">Sabel</span></span>, <span class="highwire-citation-author" data-delta="24"><span class="nlm-given-names">Mariam</span> <span class="nlm-surname">Aboian</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">Oct 2023, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(10) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1126-1134; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A8000 </span></div>
  
  
  
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