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<meta name="DC.Title" content="Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model" />
<meta name="DC.Identifier" content="10.3174/ajnr.A4298" />
<meta name="DC.Date" content="2015-08-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients.

MATERIALS AND METHODS: Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression.

RESULTS: Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity ( R 2 = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value.

CONCLUSIONS: Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.

AIFR
:   acute invasive fungal rhinosinusitis
NPV
:   negative predictive value
PPV
:   positive predictive value" />
<meta name="DC.Contributor" content="E.H. Middlebrooks" />
<meta name="DC.Contributor" content="C.J. Frost" />
<meta name="DC.Contributor" content="R.O. De Jesus" />
<meta name="DC.Contributor" content="T.C. Massini" />
<meta name="DC.Contributor" content="I.M. Schmalfuss" />
<meta name="DC.Contributor" content="A.A. Mancuso" />
<meta name="article:published_time" content="2015-08-01" />
<meta name="article:section" content="Head &amp;amp; Neck" />
<meta name="citation_title" content="Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.&lt;/p&gt;" />
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<meta name="twitter:title" content="Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model" />
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<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/36/8.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients.

MATERIALS AND METHODS: Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression.

RESULTS: Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity ( R 2 = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value.

CONCLUSIONS: Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.

AIFR
:   acute invasive fungal rhinosinusitis
NPV
:   negative predictive value
PPV
:   positive predictive value" />
<meta name="og-title" property="og:title" content="Acute Invasive Fungal Rhinosinusitis: A Comprehensive Update of CT Findings and Design of an Effective Diagnostic Imaging Model" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/36/8/1529" />
<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Acute invasive fungal rhinosinusitis carries a high mortality rate. An easy-to-use and accurate predictive imaging model is currently lacking. We assessed the performance of various CT findings for the identification of acute invasive fungal rhinosinusitis and synthesized a simple and robust diagnostic model to serve as an easily applicable screening tool for at-risk patients.

MATERIALS AND METHODS: Two blinded neuroradiologists retrospectively graded 23 prespecified imaging abnormalities in the craniofacial region on craniofacial CT examinations from 42 patients with pathology-proven acute invasive fungal rhinosinusitis and 42 control patients proved negative for acute invasive fungal rhinosinusitis from the same high-risk population. A third blinded neuroradiologist decided discrepancies. Specificity, sensitivity, positive predictive value, and negative predictive value were determined for all individual variables. The 23 variables were evaluated for intercorrelations and univariate correlations and were interrogated by using stepwise linear regression.

RESULTS: Given the low predictive value of any individual variable, a 7-variable model (periantral fat, bone dehiscence, orbital invasion, septal ulceration, pterygopalatine fossa, nasolacrimal duct, and lacrimal sac) was synthesized on the basis of multivariate analysis. The presence of abnormality involving a single variable in the model has an 87% positive predictive value, 95% negative predictive value, 95% sensitivity, and 86% specificity ( R 2 = 0.661). A positive outcome in any 2 of the model variables predicted acute invasive fungal rhinosinusitis with 100% specificity and 100% positive predictive value.

CONCLUSIONS: Our 7-variable CT-based model provides an easily applicable and robust screening tool to triage patients at risk for acute invasive fungal rhinosinusitis into a disease-positive or -negative category with a high degree of confidence.

AIFR
:   acute invasive fungal rhinosinusitis
NPV
:   negative predictive value
PPV
:   positive predictive value" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node14124--5" data-node-nid="14124" id="top-node-14124--6896722515" data-pisa="ajnr;36/8/1529" data-pisa-master="ajnr;ajnr.A4298" data-apath="/ajnr/36/8/1529.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">
  
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">E.H. Middlebrooks</span>, <span class="highwire-citation-author" data-delta="1">C.J. Frost</span>, <span class="highwire-citation-author" data-delta="2">R.O. De Jesus</span>, <span class="highwire-citation-author" data-delta="3">T.C. Massini</span>, <span class="highwire-citation-author" data-delta="4">I.M. Schmalfuss</span> and <span class="highwire-citation-author" data-delta="5">A.A. Mancuso</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">August 2015,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">36 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(8) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1529-1535; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A4298 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-14124--6896722515" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">E.H. Middlebrooks </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 (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida</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=E.H.%2BMiddlebrooks%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=Middlebrooks%20E&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%3AE.H.%2BMiddlebrooks%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="http://orcid.org/0000-0002-4418-9605" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for E.H. Middlebrooks</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">C.J. Frost </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Medical Imaging Consultants, Inc (C.J.F.), Gainesville, Florida</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=C.J.%2BFrost%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=Frost%20C&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%3AC.J.%2BFrost%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">R.O. De Jesus </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 (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida</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.O.%2BDe%2BJesus%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=De%20Jesus%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 last"><a href="/search/author1%3AR.O.%2BDe%2BJesus%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">T.C. Massini </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 (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida</div><div class='author-affiliation'><span class='nlm-sup'>c</span>North Florida/South Georgia Veterans Administration (T.C.M., I.M.S.), Gainesville, Florida.</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.C.%2BMassini%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=Massini%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 last"><a href="/search/author1%3AT.C.%2BMassini%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">I.M. Schmalfuss </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 (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida</div><div class='author-affiliation'><span class='nlm-sup'>c</span>North Florida/South Georgia Veterans Administration (T.C.M., I.M.S.), Gainesville, Florida.</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=I.M.%2BSchmalfuss%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=Schmalfuss%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 last"><a href="/search/author1%3AI.M.%2BSchmalfuss%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-5"><div class="author-tooltip-name">A.A. Mancuso </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 (E.H.M., R.O.D.J., T.C.M., I.M.S., A.A.M.), University of Florida College of Medicine, Gainesville, Florida</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.A.%2BMancuso%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=Mancuso%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%3AA.A.%2BMancuso%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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          <a href="/content/36/8" class="highwire-cite-linked-title" data-icon-position="" data-hide-link-title="0"><span class="highwire-cite-title">American Journal of Neuroradiology</span></a>      
      
        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 36</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 8 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Aug 2015 </div></div>
      
      
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">E.H.</span> <span class="nlm-surname">Middlebrooks</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.J.</span> <span class="nlm-surname">Frost</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">R.O.</span> <span class="nlm-surname">De Jesus</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">T.C.</span> <span class="nlm-surname">Massini</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">I.M.</span> <span class="nlm-surname">Schmalfuss</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">A.A.</span> <span class="nlm-surname">Mancuso</span></span></span></div>
  
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">E.H.</span> <span class="nlm-surname">Middlebrooks</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.J.</span> <span class="nlm-surname">Frost</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">R.O.</span> <span class="nlm-surname">De Jesus</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">T.C.</span> <span class="nlm-surname">Massini</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">I.M.</span> <span class="nlm-surname">Schmalfuss</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">A.A.</span> <span class="nlm-surname">Mancuso</span></span></span></div>
  
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