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    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">January 2023,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">44 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">E1; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A7715 </span></div>
  
  
  
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    <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article fulltext-view "><span class="highwire-journal-article-marker-start"></span><p id="p-1">This is a correction to Yogananda CG, Shah BR, Nalawade SS, et al. <strong>MRI-based deep-learning method for determining glioma <em>MGMT</em> promoter methylation status.</strong> <em>AJNR Am J Neuroradiol</em> 2021;42:<a href="/lookup/volpage/42/845">845–52</a> [10.3174/ajnr.A7029] [33664111]</p><p id="p-2">There was an error in the Python code for the 3-fold cross-validation procedure. This resulted in the use of the training cases instead of the set-aside test cases for the testing procedure for molecular marker accuracy. This caused our reported accuracies from the TCIA/TCGA data set to be artificially inflated. The corrected accuracies for the <a id="xref-table-wrap-1-1" class="xref-table" href="#T1">Table</a> (computed using nnU-Net<sup><a id="xref-ref-1-1" class="xref-bibr" href="#ref-1">1</a></sup>), along with the updated receiver operating characteristic (ROC) curve for <a id="xref-table-wrap-1-2" class="xref-table" href="#T1">Fig 3</a> are provided here. The updated accuracies do not outperform other reported methods for <em>MGMT</em> molecular marker prediction using MR imaging.</p><div id="T1" class="table pos-anchor"><div class="table-inline"><table frame="hsides" id="table-1"><thead valign="bottom" id="thead-1" class="table-vbottom"><tr id="tr-1"><th align="center" rowspan="2" colspan="1" id="th-1" class="table-center">Fold Description</th><th colspan="3" align="center" rowspan="1" id="th-2" class="table-center table-bborder"><em>MGMT</em>-Net</th></tr><tr id="tr-2"><th align="left" rowspan="1" colspan="1" id="th-3" class="table-left">% Accuracy</th><th align="center" rowspan="1" colspan="1" id="th-4" class="table-center">AUC</th><th align="center" rowspan="1" colspan="1" id="th-5" class="table-center">Dice Score</th></tr></thead><tbody valign="top" id="tbody-1" class="table-vtop"><tr id="tr-3"><td rowspan="1" colspan="1" id="td-1">Fold no.</td><td align="left" rowspan="1" colspan="1" id="td-2" class="table-left"></td><td align="center" rowspan="1" colspan="1" id="td-3" class="table-center"></td><td align="center" rowspan="1" colspan="1" id="td-4" class="table-center"></td></tr><tr id="tr-4"><td rowspan="1" colspan="1" id="td-5"> Fold 1</td><td align="char" char="." rowspan="1" colspan="1" id="td-6" class="table-char">59.75</td><td align="char" char="." rowspan="1" colspan="1" id="td-7" class="table-char">0.4966</td><td align="char" char="." rowspan="1" colspan="1" id="td-8" class="table-char">0.7906</td></tr><tr id="tr-5"><td rowspan="1" colspan="1" id="td-9"> Fold 2</td><td align="char" char="." rowspan="1" colspan="1" id="td-10" class="table-char">73.49</td><td align="char" char="." rowspan="1" colspan="1" id="td-11" class="table-char">0.6588</td><td align="char" char="." rowspan="1" colspan="1" id="td-12" class="table-char">0.7725</td></tr><tr id="tr-6"><td rowspan="1" colspan="1" id="td-13"> Fold 3</td><td align="char" char="." rowspan="1" colspan="1" id="td-14" class="table-char">64.63</td><td align="char" char="." rowspan="1" colspan="1" id="td-15" class="table-char">0.5854</td><td align="char" char="." rowspan="1" colspan="1" id="td-16" class="table-char">0.7874</td></tr><tr id="tr-7"><td rowspan="1" colspan="1" id="td-17">Average</td><td align="char" char="(" rowspan="1" colspan="1" id="td-18" class="table-char">65.95 (SD, 0.06)</td><td align="char" char="(" rowspan="1" colspan="1" id="td-19" class="table-char">0.5802 (SD, 0.081)</td><td align="char" char="(" rowspan="1" colspan="1" id="td-20" class="table-char">0.7835 (SD, 0.009)</td></tr></tbody></table><div class="table-foot"></div></div><div class="table-caption"><p id="p-3" class="first-child">Cross-validation results</p><div class="sb-div caption-clear"></div></div></div><div id="F1" class="fig pos-anchor  odd"><div class="highwire-figure"><div class="fig-inline-img-wrapper"><div class="fig-inline-img"><span class="hw-responsive-img"><img class="highwire-fragment fragment-image lazyload" alt="FIG 3." src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="http://ajnr-stage2.stage.highwire.org/content/ajnr/44/1/E1/F1.medium.gif" width="440" height="427"/><noscript><img class="highwire-fragment fragment-image" alt="FIG 3." src="http://ajnr-stage2.stage.highwire.org/content/ajnr/44/1/E1/F1.medium.gif" width="440" height="427"/></noscript></span></div></div></div><div class="fig-caption" xmlns:xhtml="http://www.w3.org/1999/xhtml"><span class="fig-label">FIG 3.</span> <p id="p-4" class="first-child">ROC analysis for <em>MGMT</em>-net. Separate curves are plotted for each cross-validation fold along with corresponding area under the curve (AUC) values.</p><div class="sb-div caption-clear"></div></div></div><div class="section ref-list" id="ref-list-1"><h2 class="">Reference</h2><ol class="cit-list ref-use-labels"><li><span class="ref-label">1.</span><a class="rev-xref-ref" href="#xref-ref-1-1" title="View reference 1. in text" id="ref-1">↵</a><div class="cit ref-cit ref-journal" id="cit-44.1.E1.1" data-doi="10.1038/s41592-020-01008-z"><div class="cit-metadata"><ol class="cit-auth-list"><li><span class="cit-auth"><span class="cit-name-surname">Isensee</span>  <span class="cit-name-given-names">F</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Jaeger</span>  <span class="cit-name-given-names">PF</span></span>, </li><li><span class="cit-auth"><span class="cit-name-surname">Kohl</span>  <span class="cit-name-given-names">SAA</span></span>, <span class="cit-etal">et al</span></li></ol><cite>. <span class="cit-article-title">nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation</span>. <abbr class="cit-jnl-abbrev">Nat Methods</abbr> <span class="cit-pub-date">2021</span>;<span class="cit-vol">18</span>:<span class="cit-fpage">203</span>–<span class="cit-lpage">11</span><span class="cit-pub-id-sep cit-pub-id-doi-sep"> </span><span class="cit-pub-id cit-pub-id-doi"><span class="cit-pub-id-scheme-doi">doi:</span>10.1038/s41592-020-01008-z</span><span class="cit-pub-id-sep cit-pub-id-pmid-sep"> </span><span class="cit-pub-id cit-pub-id-pmid"><span class="cit-pub-id-scheme-pmid">pmid:</span>33288961</span></cite></div><div class="cit-extra"><a href="/lookup/external-ref?access_num=10.1038/s41592-020-01008-z&amp;link_type=DOI" class="cit-ref-sprinkles cit-ref-sprinkles-doi cit-ref-sprinkles-crossref"><span>CrossRef</span></a><a href="/lookup/external-ref?access_num=33288961&amp;link_type=MED&amp;atom=%2Fajnr%2F44%2F1%2FE1.atom" class="cit-ref-sprinkles cit-ref-sprinkles-medline"><span>PubMed</span></a></div></div></li></ol></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">© 2023 by American Journal of Neuroradiology</li></ul><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div>  </div>

  
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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 1 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Jan 2023 </div></div>
      
      
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