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<meta name="DC.Title" content="Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment" />
<meta name="DC.Identifier" content="10.3174/ajnr.A7473" />
<meta name="DC.Date" content="2022-04-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE: Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES: Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION: From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS: Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS: The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898–0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS: Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS: Machine learning–based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
ML
:   machine learning
PCNSL
:   primary CNS lymphoma
PRISMA
:   Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROBAST
:   Prediction model study Risk Of Bias Assessment Tool
TRIPOD
:   Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" />
<meta name="DC.Contributor" content="G.I. Cassinelli Petersen" />
<meta name="DC.Contributor" content="J. Shatalov" />
<meta name="DC.Contributor" content="T. Verma" />
<meta name="DC.Contributor" content="W.R. Brim" />
<meta name="DC.Contributor" content="H. Subramanian" />
<meta name="DC.Contributor" content="A. Brackett" />
<meta name="DC.Contributor" content="R.C. Bahar" />
<meta name="DC.Contributor" content="S. Merkaj" />
<meta name="DC.Contributor" content="T. Zeevi" />
<meta name="DC.Contributor" content="L.H. Staib" />
<meta name="DC.Contributor" content="J. Cui" />
<meta name="DC.Contributor" content="A. Omuro" />
<meta name="DC.Contributor" content="R.A. Bronen" />
<meta name="DC.Contributor" content="A. Malhotra" />
<meta name="DC.Contributor" content="M.S. Aboian" />
<meta name="article:published_time" content="2022-04-01" />
<meta name="article:section" content="Adult Brain" />
<meta name="citation_title" content="Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND:&lt;/h3&gt;
&lt;p&gt;Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.&lt;/p&gt;&lt;h3&gt;PURPOSE:&lt;/h3&gt;
&lt;p&gt;Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.&lt;/p&gt;&lt;h3&gt;DATA SOURCES:&lt;/h3&gt;
&lt;p&gt;Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.&lt;/p&gt;&lt;h3&gt;STUDY SELECTION:&lt;/h3&gt;
&lt;p&gt;From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.&lt;/p&gt;&lt;h3&gt;DATA ANALYSIS:&lt;/h3&gt;
&lt;p&gt;Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.&lt;/p&gt;&lt;h3&gt;DATA SYNTHESIS:&lt;/h3&gt;
&lt;p&gt;The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898–0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.&lt;/p&gt;&lt;h3&gt;LIMITATIONS:&lt;/h3&gt;
&lt;p&gt;Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Machine learning–based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.&lt;/p&gt;" />
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<meta name="twitter:title" content="Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment" />
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<meta name="twitter:description" content="BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE: Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES: Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION: From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS: Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS: The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898–0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS: Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS: Machine learning–based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
ML
:   machine learning
PCNSL
:   primary CNS lymphoma
PRISMA
:   Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROBAST
:   Prediction model study Risk Of Bias Assessment Tool
TRIPOD
:   Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" />
<meta name="og-title" property="og:title" content="Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/43/4/526" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors.

PURPOSE: Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance.

DATA SOURCES: Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

STUDY SELECTION: From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.

DATA ANALYSIS: Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool.

DATA SYNTHESIS: The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898–0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.

LIMITATIONS: Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.

CONCLUSIONS: Machine learning–based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.

AI
:   artificial intelligence
AUC
:   area under the receiver operating characteristic curve
CNN
:   convolutional neural network
ML
:   machine learning
PCNSL
:   primary CNS lymphoma
PRISMA
:   Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROBAST
:   Prediction model study Risk Of Bias Assessment Tool
TRIPOD
:   Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node96526--7" data-node-nid="96526" id="top-node-96526--81611392425" data-pisa="ajnr;43/4/526" data-pisa-master="ajnr;ajnr.A7473" data-apath="/ajnr/43/4/526.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">Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">G.I. Cassinelli Petersen</span>, <span class="highwire-citation-author" data-delta="1">J. Shatalov</span>, <span class="highwire-citation-author" data-delta="2">T. Verma</span>, <span class="highwire-citation-author" data-delta="3">W.R. Brim</span>, <span class="highwire-citation-author" data-delta="4">H. Subramanian</span>, <span class="highwire-citation-author" data-delta="5">A. Brackett</span>, <span class="highwire-citation-author" data-delta="6">R.C. Bahar</span>, <span class="highwire-citation-author" data-delta="7">S. Merkaj</span>, <span class="highwire-citation-author" data-delta="8">T. Zeevi</span>, <span class="highwire-citation-author" data-delta="9">L.H. Staib</span>, <span class="highwire-citation-author" data-delta="10">J. Cui</span>, <span class="highwire-citation-author" data-delta="11">A. Omuro</span>, <span class="highwire-citation-author" data-delta="12">R.A. Bronen</span>, <span class="highwire-citation-author" data-delta="13">A. Malhotra</span> and <span class="highwire-citation-author" data-delta="14">M.S. 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">April 2022,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">526-533; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7473 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-96526--81611392425" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">G.I. Cassinelli Petersen </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</div><div class='author-affiliation'><span class='nlm-sup'>d</span>Universitätsmedizin Göttingen (G.I.C.P.), Göttingen, 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=G.I.%2BCassinelli%2BPetersen%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=Cassinelli%20Petersen%20G&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AG.I.%2BCassinelli%2BPetersen%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/0000-0002-4546-0567" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for G.I. Cassinelli Petersen</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">J. Shatalov </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>University of Richmond (J.S.), Richmond, Virginia</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.%2BShatalov%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=Shatalov%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.%2BShatalov%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/0000-0003-3927-2639" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Shatalov</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">T. 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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</div><div class='author-affiliation'><span class='nlm-sup'>f</span>New York University (T.V.), New York, New York</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=T.%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%3AT.%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/0000-0002-1260-5002" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for T. Verma</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">W.R. Brim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>g</span>Whiting School of Engineering (W.R.B.), Johns Hopkins University, Baltimore, Maryland</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.R.%2BBrim%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=Brim%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.R.%2BBrim%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/0000-0001-5921-1062" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for W.R. Brim</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">H. Subramanian </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.%2BSubramanian%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=Subramanian%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.%2BSubramanian%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/0000-0001-9173-6248" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for H. Subramanian</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">A. Brackett </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Cushing/Whitney Medical Library (A.B.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=A.%2BBrackett%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=Brackett%20A&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AA.%2BBrackett%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/0000-0003-3801-833X" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Brackett</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">R.C. Bahar </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.C.%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%3AR.C.%2BBahar%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/0000-0001-6144-8069" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R.C. Bahar</a></li></ul></div><div class="author-tooltip-7"><div class="author-tooltip-name">S. Merkaj </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.%2BMerkaj%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=Merkaj%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.%2BMerkaj%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/0000-0003-1147-4968" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S. Merkaj</a></li></ul></div><div class="author-tooltip-8"><div class="author-tooltip-name">T. Zeevi </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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=T.%2BZeevi%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=Zeevi%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%3AT.%2BZeevi%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/0000-0001-8917-7061" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for T. Zeevi</a></li></ul></div><div class="author-tooltip-9"><div class="author-tooltip-name">L.H. Staib </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.H.%2BStaib%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=Staib%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.H.%2BStaib%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/0000-0002-9516-5136" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for L.H. Staib</a></li></ul></div><div class="author-tooltip-10"><div class="author-tooltip-name">J. Cui </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.%2BCui%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=Cui%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.%2BCui%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/0000-0003-4638-6466" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J. Cui</a></li></ul></div><div class="author-tooltip-11"><div class="author-tooltip-name">A. Omuro </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Department of Neurology (A.O.), 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=A.%2BOmuro%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=Omuro%20A&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AA.%2BOmuro%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/0000-0003-4299-3664" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Omuro</a></li></ul></div><div class="author-tooltip-12"><div class="author-tooltip-name">R.A. Bronen </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</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.A.%2BBronen%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=Bronen%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.A.%2BBronen%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/0000-0002-8127-3662" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for R.A. Bronen</a></li></ul></div><div class="author-tooltip-13"><div class="author-tooltip-name">A. Malhotra </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 (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=A.%2BMalhotra%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=Malhotra%20A&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AA.%2BMalhotra%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/0000-0001-9223-6640" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for A. Malhotra</a></li></ul></div><div class="author-tooltip-14"><div class="author-tooltip-name">M.S. 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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div xmlns:xhtml="http://www.w3.org/1999/xhtml" class="contributors"><ol class="contributor-list" id="contrib-group-1"><li class="contributor" id="contrib-1"><span class="name">G.I. Cassinelli Petersen</span><a id="xref-aff-1-1" class="xref-aff" href="#aff-1">a</a><span class="xref-sep">,</span><a id="xref-aff-4-1" class="xref-aff" href="#aff-4">d</a>, </li><li class="contributor" id="contrib-2"><span class="name">J. Shatalov</span><a id="xref-aff-5-1" class="xref-aff" href="#aff-5">e</a>, </li><li class="contributor" id="contrib-3"><span class="name">T. Verma</span><a id="xref-aff-1-2" class="xref-aff" href="#aff-1">a</a><span class="xref-sep">,</span><a id="xref-aff-6-1" class="xref-aff" href="#aff-6">f</a>, </li><li class="contributor" id="contrib-4"><span class="name">W.R. Brim</span><a id="xref-aff-7-1" class="xref-aff" href="#aff-7">g</a>, </li><li class="contributor" id="contrib-5"><span class="name">H. Subramanian</span><a id="xref-aff-1-3" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-6"><span class="name">A. Brackett</span><a id="xref-aff-2-1" class="xref-aff" href="#aff-2">b</a>, </li><li class="contributor" id="contrib-7"><span class="name">R.C. Bahar</span><a id="xref-aff-1-4" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-8"><span class="name">S. Merkaj</span><a id="xref-aff-1-5" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-9"><span class="name">T. Zeevi</span><a id="xref-aff-1-6" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-10"><span class="name">L.H. Staib</span><a id="xref-aff-1-7" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-11"><span class="name">J. Cui</span><a id="xref-aff-1-8" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-12"><span class="name">A. Omuro</span><a id="xref-aff-3-1" class="xref-aff" href="#aff-3">c</a>, </li><li class="contributor" id="contrib-13"><span class="name">R.A. Bronen</span><a id="xref-aff-1-9" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-14"><span class="name">A. Malhotra</span><a id="xref-aff-1-10" class="xref-aff" href="#aff-1">a</a> and </li><li class="last" id="contrib-15"><span class="name">M.S. Aboian</span><a id="xref-aff-1-11" class="xref-aff" href="#aff-1">a</a></li></ol><ol class="affiliation-list"><li class="aff"><a id="aff-1" name="aff-1"></a><address><sup>a</sup>From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)</address></li><li class="aff"><a id="aff-2" name="aff-2"></a><address><sup>b</sup>Cushing/Whitney Medical Library (A.B.)</address></li><li class="aff"><a id="aff-3" name="aff-3"></a><address><sup>c</sup>Department of Neurology (A.O.), Yale School of Medicine, New Haven, Connecticut</address></li><li class="aff"><a id="aff-4" name="aff-4"></a><address><sup>d</sup>Universitätsmedizin Göttingen (G.I.C.P.), Göttingen, Germany</address></li><li class="aff"><a id="aff-5" name="aff-5"></a><address><sup>e</sup>University of Richmond (J.S.), Richmond, Virginia</address></li><li class="aff"><a id="aff-6" name="aff-6"></a><address><sup>f</sup>New York University (T.V.), New York, New York</address></li><li class="aff"><a id="aff-7" name="aff-7"></a><address><sup>g</sup>Whiting School of Engineering (W.R.B.), Johns Hopkins University, Baltimore, Maryland</address></li></ol><ol class="corresp-list"><li class="corresp" id="corresp-1">Please address correspondence to Mariam Aboian, MD, PhD, <span class="addr-line">Department of Radiology, Yale School of Medicine, 333 Cedar St, New Haven, 06510 CT</span>; e-mail: <span class="em-link"><span class="em-addr">mariam.aboian{at}yale.edu</span></span>; <a href="https://mobile.twitter.com/GabrielCassine1">@GabrielCassine1</a></li></ol></div></div><a href="http://ajnr-stage2.stage.highwire.org/content/43/4/526.full" class="hw-link hw-link-article-full-text" data-icon-position="" data-hide-link-title="0">View Full Text</a></div>  </div>

  
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 43</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 4 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Apr 2022 </div></div>
      
      
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      <div class="highwire-cite-title">Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment</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">G.I.</span> <span class="nlm-surname">Cassinelli Petersen</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Shatalov</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">T.</span> <span class="nlm-surname">Verma</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">W.R.</span> <span class="nlm-surname">Brim</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">H.</span> <span class="nlm-surname">Subramanian</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Brackett</span></span>, <span class="highwire-citation-author" data-delta="6"><span class="nlm-given-names">R.C.</span> <span class="nlm-surname">Bahar</span></span>, <span class="highwire-citation-author" data-delta="7"><span class="nlm-given-names">S.</span> <span class="nlm-surname">Merkaj</span></span>, <span class="highwire-citation-author" data-delta="8"><span class="nlm-given-names">T.</span> <span class="nlm-surname">Zeevi</span></span>, <span class="highwire-citation-author" data-delta="9"><span class="nlm-given-names">L.H.</span> <span class="nlm-surname">Staib</span></span>, <span class="highwire-citation-author" data-delta="10"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Cui</span></span>, <span class="highwire-citation-author" data-delta="11"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Omuro</span></span>, <span class="highwire-citation-author" data-delta="12"><span class="nlm-given-names">R.A.</span> <span class="nlm-surname">Bronen</span></span>, <span class="highwire-citation-author" data-delta="13"><span class="nlm-given-names">A.</span> <span class="nlm-surname">Malhotra</span></span>, <span class="highwire-citation-author" data-delta="14"><span class="nlm-given-names">M.S.</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">Apr 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">526-533; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7473 </span></div>
  
  
  
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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Apr 2022, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">43 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">526-533; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A7473 </span></div>
  
  
  
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