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<meta name="DC.Title" content="Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas" />
<meta name="DC.Date" content="2007-04-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="Copyright © American Society of Neuroradiology" />
<meta name="DC.AccessRights" content="restricted" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Histogram analysis can be applied to dynamic susceptibility contrast (DSC) perfusion MR imaging datasets and can be as effective as traditional region-of-interest (ROI) measurements of relative cerebral blood volume (rCBV), an operator-dependent method. We compare the routine ROI method with histogram analysis in the grading of glial neoplasms.

MATERIALS AND METHODS: Ninety-two patients underwent conventional and DSC MR imaging. Routine rCBV (rCBVmax) measurements were obtained from ROIs of the maximal abnormality within the glioma. Histogram analysis rCBVT was performed with an ROI drawn around the maximal tumor diameter. Spearman rank correlations measured associations among glioma grade, rCBVmax, and histogram measures. Mann-Whitney tests compared grade with respect to rCBV and histogram measures. Logistic regression and McNemar test compared the utility of rCBVmax and histogram measures for detecting high grade gliomas.

RESULTS: Routine rCBVmax analysis showed significant correlation with grade ( r = 0.734, P &lt; .001). Histogram rCBVT metrics showed significant correlation with grade ( P &lt; .008); the 3 highest were rCBVT SD, SD50, and mean25 ( r = 0.718, 0.684, and 0.683, respectively). Grade could be predicted by rCBVmax ( P &lt; .001) as well as rCBVT ( P &lt; .008). Three rCBVT histogram measures (SD, SD25, and SD50) detected high-grade glioma with significantly higher specificity than rCBVmax when the diagnostic tests were constrained to have at least 95% sensitivity.

CONCLUSION: rCBVT histogram analysis is as effective as rCBVmax analysis in the correlation with glioma grade. Inexperienced operators may obtain perfusion metrics using histogram analyses that are comparable with those obtained by experienced operators using ROI analysis." />
<meta name="DC.Contributor" content="M. Law" />
<meta name="DC.Contributor" content="R. Young" />
<meta name="DC.Contributor" content="J. Babb" />
<meta name="DC.Contributor" content="E. Pollack" />
<meta name="DC.Contributor" content="G. Johnson" />
<meta name="article:published_time" content="2007-04-01" />
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<meta name="citation_title" content="Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas" />
<meta name="citation_abstract" lang="en" content="&lt;p&gt;&lt;b&gt;BACKGROUND AND PURPOSE:&lt;/b&gt; Histogram analysis can be applied to dynamic susceptibility contrast (DSC) perfusion MR imaging datasets and can be as effective as traditional region-of-interest (ROI) measurements of relative cerebral blood volume (rCBV), an operator-dependent method. We compare the routine ROI method with histogram analysis in the grading of glial neoplasms.&lt;/p&gt;&lt;p&gt;&lt;b&gt;MATERIALS AND METHODS:&lt;/b&gt; Ninety-two patients underwent conventional and DSC MR imaging. Routine rCBV (rCBV&lt;sub&gt;max&lt;/sub&gt;) measurements were obtained from ROIs of the maximal abnormality within the glioma. Histogram analysis rCBV&lt;sub&gt;T&lt;/sub&gt; was performed with an ROI drawn around the maximal tumor diameter. Spearman rank correlations measured associations among glioma grade, rCBV&lt;sub&gt;max&lt;/sub&gt;, and histogram measures. Mann-Whitney tests compared grade with respect to rCBV and histogram measures. Logistic regression and McNemar test compared the utility of rCBV&lt;sub&gt;max&lt;/sub&gt; and histogram measures for detecting high grade gliomas.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS:&lt;/b&gt; Routine rCBV&lt;sub&gt;max&lt;/sub&gt; analysis showed significant correlation with grade (&lt;i&gt;r&lt;/i&gt; = 0.734, &lt;i&gt;P&lt;/i&gt; &amp;lt; .001). Histogram rCBV&lt;sub&gt;T&lt;/sub&gt; metrics showed significant correlation with grade (&lt;i&gt;P&lt;/i&gt; &amp;lt; .008); the 3 highest were rCBV&lt;sub&gt;T&lt;/sub&gt; SD, SD&lt;sub&gt;50&lt;/sub&gt;, and mean&lt;sub&gt;25&lt;/sub&gt; (&lt;i&gt;r&lt;/i&gt; = 0.718, 0.684, and 0.683, respectively). Grade could be predicted by rCBV&lt;sub&gt;max&lt;/sub&gt; (&lt;i&gt;P&lt;/i&gt; &amp;lt; .001) as well as rCBV&lt;sub&gt;T&lt;/sub&gt; (&lt;i&gt;P&lt;/i&gt; &amp;lt; .008). Three rCBVT histogram measures (SD, SD&lt;sub&gt;25&lt;/sub&gt;, and SD&lt;sub&gt;50&lt;/sub&gt;) detected high-grade glioma with significantly higher specificity than rCBV&lt;sub&gt;max&lt;/sub&gt; when the diagnostic tests were constrained to have at least 95% sensitivity.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION:&lt;/b&gt; rCBV&lt;sub&gt;T&lt;/sub&gt; histogram analysis is as effective as rCBV&lt;sub&gt;max&lt;/sub&gt; analysis in the correlation with glioma grade. Inexperienced operators may obtain perfusion metrics using histogram analyses that are comparable with those obtained by experienced operators using ROI analysis.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_mjid" content="ajnr;28/4/761" />
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<meta name="twitter:title" content="Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Histogram analysis can be applied to dynamic susceptibility contrast (DSC) perfusion MR imaging datasets and can be as effective as traditional region-of-interest (ROI) measurements of relative cerebral blood volume (rCBV), an operator-dependent method. We compare the routine ROI method with histogram analysis in the grading of glial neoplasms.

MATERIALS AND METHODS: Ninety-two patients underwent conventional and DSC MR imaging. Routine rCBV (rCBVmax) measurements were obtained from ROIs of the maximal abnormality within the glioma. Histogram analysis rCBVT was performed with an ROI drawn around the maximal tumor diameter. Spearman rank correlations measured associations among glioma grade, rCBVmax, and histogram measures. Mann-Whitney tests compared grade with respect to rCBV and histogram measures. Logistic regression and McNemar test compared the utility of rCBVmax and histogram measures for detecting high grade gliomas.

RESULTS: Routine rCBVmax analysis showed significant correlation with grade ( r = 0.734, P &lt; .001). Histogram rCBVT metrics showed significant correlation with grade ( P &lt; .008); the 3 highest were rCBVT SD, SD50, and mean25 ( r = 0.718, 0.684, and 0.683, respectively). Grade could be predicted by rCBVmax ( P &lt; .001) as well as rCBVT ( P &lt; .008). Three rCBVT histogram measures (SD, SD25, and SD50) detected high-grade glioma with significantly higher specificity than rCBVmax when the diagnostic tests were constrained to have at least 95% sensitivity.

CONCLUSION: rCBVT histogram analysis is as effective as rCBVmax analysis in the correlation with glioma grade. Inexperienced operators may obtain perfusion metrics using histogram analyses that are comparable with those obtained by experienced operators using ROI analysis." />
<meta name="og-title" property="og:title" content="Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas" />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Histogram analysis can be applied to dynamic susceptibility contrast (DSC) perfusion MR imaging datasets and can be as effective as traditional region-of-interest (ROI) measurements of relative cerebral blood volume (rCBV), an operator-dependent method. We compare the routine ROI method with histogram analysis in the grading of glial neoplasms.

MATERIALS AND METHODS: Ninety-two patients underwent conventional and DSC MR imaging. Routine rCBV (rCBVmax) measurements were obtained from ROIs of the maximal abnormality within the glioma. Histogram analysis rCBVT was performed with an ROI drawn around the maximal tumor diameter. Spearman rank correlations measured associations among glioma grade, rCBVmax, and histogram measures. Mann-Whitney tests compared grade with respect to rCBV and histogram measures. Logistic regression and McNemar test compared the utility of rCBVmax and histogram measures for detecting high grade gliomas.

RESULTS: Routine rCBVmax analysis showed significant correlation with grade ( r = 0.734, P &lt; .001). Histogram rCBVT metrics showed significant correlation with grade ( P &lt; .008); the 3 highest were rCBVT SD, SD50, and mean25 ( r = 0.718, 0.684, and 0.683, respectively). Grade could be predicted by rCBVmax ( P &lt; .001) as well as rCBVT ( P &lt; .008). Three rCBVT histogram measures (SD, SD25, and SD50) detected high-grade glioma with significantly higher specificity than rCBVmax when the diagnostic tests were constrained to have at least 95% sensitivity.

CONCLUSION: rCBVT histogram analysis is as effective as rCBVmax analysis in the correlation with glioma grade. Inexperienced operators may obtain perfusion metrics using histogram analyses that are comparable with those obtained by experienced operators using ROI analysis." />
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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 28</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 4 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">April 2007 </div></div>
      
      
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      <div class="highwire-cite-title">Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas</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">M.</span> <span class="nlm-surname">Law</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Young</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Babb</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">E.</span> <span class="nlm-surname">Pollack</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Johnson</span></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">Apr 2007, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">28 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">761-766; </span></div>
  
  
  
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      <div class="highwire-cite-title">Histogram Analysis versus Region of Interest Analysis of Dynamic Susceptibility Contrast Perfusion MR Imaging Data in the Grading of Cerebral Gliomas</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">M.</span> <span class="nlm-surname">Law</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">R.</span> <span class="nlm-surname">Young</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">J.</span> <span class="nlm-surname">Babb</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">E.</span> <span class="nlm-surname">Pollack</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">G.</span> <span class="nlm-surname">Johnson</span></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">Apr 2007, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">28 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">761-766; </span></div>
  
  
  
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