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<meta name="DC.Title" content="Tracking Tumor Growth Rates in Patients with Malignant Gliomas: A Test of Two Algorithms" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference.

METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan.

RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not.

CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well." />
<meta name="DC.Contributor" content="Sean M. Haney" />
<meta name="DC.Contributor" content="Paul M. Thompson" />
<meta name="DC.Contributor" content="Timothy F. Cloughesy" />
<meta name="DC.Contributor" content="Jeffry R. Alger" />
<meta name="DC.Contributor" content="Arthur W. Toga" />
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<meta name="citation_title" content="Tracking Tumor Growth Rates in Patients with Malignant Gliomas: A Test of Two Algorithms" />
<meta name="citation_abstract" lang="en" content="&lt;p&gt;&lt;i&gt;BACKGROUND AND PURPOSE:&lt;/i&gt; Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference.&lt;/p&gt;&lt;p&gt;&lt;i&gt;METHODS:&lt;/i&gt; Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan.&lt;/p&gt;&lt;p&gt;&lt;i&gt;RESULTS:&lt;/i&gt; Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not.&lt;/p&gt;&lt;p&gt;&lt;i&gt;CONCLUSION:&lt;/i&gt; The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well.&lt;/p&gt;" />
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<meta name="citation_author" content="Sean M. Haney" />
<meta name="citation_author" content="Paul M. Thompson" />
<meta name="citation_author" content="Timothy F. Cloughesy" />
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<meta name="twitter:title" content="Tracking Tumor Growth Rates in Patients with Malignant Gliomas: A Test of Two Algorithms" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference.

METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan.

RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not.

CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well." />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference.

METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan.

RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not.

CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well." />
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      <h1 class="highwire-cite-title" id="page-title">Tracking Tumor Growth Rates in Patients with Malignant Gliomas: A Test of Two Algorithms</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">Sean M. Haney</span>, <span class="highwire-citation-author" data-delta="1">Paul M. Thompson</span>, <span class="highwire-citation-author" data-delta="2">Timothy F. Cloughesy</span>, <span class="highwire-citation-author" data-delta="3">Jeffry R. Alger</span> and <span class="highwire-citation-author" data-delta="4">Arthur W. Toga</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">January 2001,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">22 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(1) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">73-82; </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-1979--8309595402" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Sean M. Haney </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=Sean%2BM.%2BHaney%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=Haney%20SM&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%3ASean%2BM.%2BHaney%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-1"><div class="author-tooltip-name">Paul M. Thompson </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=Paul%2BM.%2BThompson%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=Thompson%20PM&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%3APaul%2BM.%2BThompson%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-2"><div class="author-tooltip-name">Timothy F. Cloughesy </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=Timothy%2BF.%2BCloughesy%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=Cloughesy%20TF&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%3ATimothy%2BF.%2BCloughesy%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">Jeffry R. Alger </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=Jeffry%2BR.%2BAlger%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=Alger%20JR&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%3AJeffry%2BR.%2BAlger%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-4"><div class="author-tooltip-name">Arthur W. Toga </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=Arthur%2BW.%2BToga%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=Toga%20AW&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%3AArthur%2BW.%2BToga%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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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">Sean M. Haney</span><a id="xref-aff-1-1" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-2"><span class="name">Paul M. Thompson</span><a id="xref-aff-1-2" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-3"><span class="name">Timothy F. Cloughesy</span><a id="xref-aff-1-3" class="xref-aff" href="#aff-1">a</a>, </li><li class="contributor" id="contrib-4"><span class="name">Jeffry R. Alger</span><a id="xref-aff-1-4" class="xref-aff" href="#aff-1">a</a> and </li><li class="last" id="contrib-5"><span class="name">Arthur W. Toga</span><a id="xref-fn-2-1" class="xref-fn" href="#fn-2"></a><a id="xref-aff-1-5" 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 Laboratory of Neuro Imaging, Department of Neurology, Division of Brain Mapping (S.M.H., P.M.T., A.W.T.), the Neuro Oncology Program (T.F.C.), the Henry E. Singleton Brain Cancer Research Program (T.F.C.), and the Department of Radiological Sciences (J.R.A.), University of California at Los Angeles School of Medicine.</address></li></ol></div></div><a href="http://ajnr-stage2.stage.highwire.org/content/22/1/73.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. 22</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 1 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Jan 2001 </div></div>
      
      
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