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<meta name="DC.Title" content="A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma" />
<meta name="DC.Identifier" content="10.3174/ajnr.A6066" />
<meta name="DC.Date" content="2019-06-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: An accurate and comprehensive assessment of lymph node metastasis in patients with head and neck squamous cell cancer is crucial in daily practice. This study constructed a predictive model with a risk scoring system based on CT characteristics of lymph nodes and tumors for patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis.

MATERIALS AND METHODS: Data included 476 cervical lymph nodes from 191 patients with head and neck squamous cell carcinoma from a historical cohort. We analyzed preoperative CT images of lymph nodes, including diameter, ratio of long-to-short axis diameter, necrosis, conglomeration, infiltration to adjacent soft tissue, laterality and T-stage of the primary tumor. The reference standard comprised pathologic results. Multivariable logistic regression analysis was performed to develop the risk scoring system. Internal validation was performed with 1000-iteration bootstrapping.

RESULTS: Shortest axial diameter, ratio of long-to-short axis diameter, necrosis, and T-stage were used to develop a 9-point risk scoring system. The risk of malignancy ranged from 7.3% to 99.8%, which was positively associated with increased scores. Areas under the curve of the risk scoring systems were 0.886 (95% CI, 0.881–0.920) and 0.879 (95% CI, 0.845–0.914) in internal validation. The Hosmer–Lemeshow goodness-of-fit test indicated that the risk scoring system was well-calibrated ( P = .160).

CONCLUSIONS: We developed a comprehensive and simple risk scoring system using CT characteristics in patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis. It could facilitate decision-making in daily practice.

AUC
:   area under the curve
HNSCC
:   head and neck squamous cell carcinoma
L/S
:   long-to-short axis" />
<meta name="DC.Contributor" content="M.S. Chung" />
<meta name="DC.Contributor" content="Y.J. Choi" />
<meta name="DC.Contributor" content="S.O. Kim" />
<meta name="DC.Contributor" content="Y.S. Lee" />
<meta name="DC.Contributor" content="J.Y. Hong" />
<meta name="DC.Contributor" content="J.H. Lee" />
<meta name="DC.Contributor" content="J.H. Baek" />
<meta name="article:published_time" content="2019-06-01" />
<meta name="article:section" content="Head &amp;amp; Neck" />
<meta name="citation_title" content="A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;An accurate and comprehensive assessment of lymph node metastasis in patients with head and neck squamous cell cancer is crucial in daily practice. This study constructed a predictive model with a risk scoring system based on CT characteristics of lymph nodes and tumors for patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis.&lt;/p&gt;&lt;h3&gt;MATERIALS AND METHODS:&lt;/h3&gt;
&lt;p&gt;Data included 476 cervical lymph nodes from 191 patients with head and neck squamous cell carcinoma from a historical cohort. We analyzed preoperative CT images of lymph nodes, including diameter, ratio of long-to-short axis diameter, necrosis, conglomeration, infiltration to adjacent soft tissue, laterality and T-stage of the primary tumor. The reference standard comprised pathologic results. Multivariable logistic regression analysis was performed to develop the risk scoring system. Internal validation was performed with 1000-iteration bootstrapping.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;Shortest axial diameter, ratio of long-to-short axis diameter, necrosis, and T-stage were used to develop a 9-point risk scoring system. The risk of malignancy ranged from 7.3% to 99.8%, which was positively associated with increased scores. Areas under the curve of the risk scoring systems were 0.886 (95% CI, 0.881–0.920) and 0.879 (95% CI, 0.845–0.914) in internal validation. The Hosmer–Lemeshow goodness-of-fit test indicated that the risk scoring system was well-calibrated (&lt;i&gt;P&lt;/i&gt; = .160).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;We developed a comprehensive and simple risk scoring system using CT characteristics in patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis. It could facilitate decision-making in daily practice.&lt;/p&gt;" />
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<meta name="twitter:title" content="A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: An accurate and comprehensive assessment of lymph node metastasis in patients with head and neck squamous cell cancer is crucial in daily practice. This study constructed a predictive model with a risk scoring system based on CT characteristics of lymph nodes and tumors for patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis.

MATERIALS AND METHODS: Data included 476 cervical lymph nodes from 191 patients with head and neck squamous cell carcinoma from a historical cohort. We analyzed preoperative CT images of lymph nodes, including diameter, ratio of long-to-short axis diameter, necrosis, conglomeration, infiltration to adjacent soft tissue, laterality and T-stage of the primary tumor. The reference standard comprised pathologic results. Multivariable logistic regression analysis was performed to develop the risk scoring system. Internal validation was performed with 1000-iteration bootstrapping.

RESULTS: Shortest axial diameter, ratio of long-to-short axis diameter, necrosis, and T-stage were used to develop a 9-point risk scoring system. The risk of malignancy ranged from 7.3% to 99.8%, which was positively associated with increased scores. Areas under the curve of the risk scoring systems were 0.886 (95% CI, 0.881–0.920) and 0.879 (95% CI, 0.845–0.914) in internal validation. The Hosmer–Lemeshow goodness-of-fit test indicated that the risk scoring system was well-calibrated ( P = .160).

CONCLUSIONS: We developed a comprehensive and simple risk scoring system using CT characteristics in patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis. It could facilitate decision-making in daily practice.

AUC
:   area under the curve
HNSCC
:   head and neck squamous cell carcinoma
L/S
:   long-to-short axis" />
<meta name="og-title" property="og:title" content="A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma" />
<meta name="og-url" property="og:url" content="http://ajnr-stage2.stage.highwire.org/content/40/6/1049" />
<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: An accurate and comprehensive assessment of lymph node metastasis in patients with head and neck squamous cell cancer is crucial in daily practice. This study constructed a predictive model with a risk scoring system based on CT characteristics of lymph nodes and tumors for patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis.

MATERIALS AND METHODS: Data included 476 cervical lymph nodes from 191 patients with head and neck squamous cell carcinoma from a historical cohort. We analyzed preoperative CT images of lymph nodes, including diameter, ratio of long-to-short axis diameter, necrosis, conglomeration, infiltration to adjacent soft tissue, laterality and T-stage of the primary tumor. The reference standard comprised pathologic results. Multivariable logistic regression analysis was performed to develop the risk scoring system. Internal validation was performed with 1000-iteration bootstrapping.

RESULTS: Shortest axial diameter, ratio of long-to-short axis diameter, necrosis, and T-stage were used to develop a 9-point risk scoring system. The risk of malignancy ranged from 7.3% to 99.8%, which was positively associated with increased scores. Areas under the curve of the risk scoring systems were 0.886 (95% CI, 0.881–0.920) and 0.879 (95% CI, 0.845–0.914) in internal validation. The Hosmer–Lemeshow goodness-of-fit test indicated that the risk scoring system was well-calibrated ( P = .160).

CONCLUSIONS: We developed a comprehensive and simple risk scoring system using CT characteristics in patients with head and neck squamous cell carcinoma to stratify the risk of lymph node metastasis. It could facilitate decision-making in daily practice.

AUC
:   area under the curve
HNSCC
:   head and neck squamous cell carcinoma
L/S
:   long-to-short axis" />
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    <div class="highwire-article-citation highwire-citation-type-highwire-article node67953--5" data-node-nid="67953" id="top-node-67953--62115145476" data-pisa="ajnr;40/6/1049" data-pisa-master="ajnr;ajnr.A6066" data-apath="/ajnr/40/6/1049.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">Head &amp; Neck</span></span></div>
  
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      <h1 class="highwire-cite-title" id="page-title">A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma</h1>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">M.S. Chung</span>, <span class="highwire-citation-author" data-delta="1">Y.J. Choi</span>, <span class="highwire-citation-author" data-delta="2">S.O. Kim</span>, <span class="highwire-citation-author" data-delta="3">Y.S. Lee</span>, <span class="highwire-citation-author" data-delta="4">J.Y. Hong</span>, <span class="highwire-citation-author" data-delta="5">J.H. Lee</span> and <span class="highwire-citation-author" data-delta="6">J.H. Baek</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">June 2019,  </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1049-1054; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata">DOI: https://doi.org/10.3174/ajnr.A6066 </span></div>
  
  
  
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<div id="hw-article-author-popups-top-node-67953--62115145476" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">M.S. Chung </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 (M.S.C.), Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea</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=M.S.%2BChung%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=Chung%20M&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%3AM.S.%2BChung%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/0000-0003-1141-9555" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for M.S. Chung</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Y.J. Choi </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Departments of Radiology and Research Institute of Radiology (Y.J.C., J.H.L., J.H.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=Y.J.%2BChoi%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=Choi%20Y&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%3AY.J.%2BChoi%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/0000-0001-7098-5042" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Y.J. Choi</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">S.O. Kim </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>c</span>Clinical Epidemiology and Biostatistics (S.O.K.)</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.O.%2BKim%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=Kim%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.O.%2BKim%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/0000-0001-9010-5460" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for S.O. Kim</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Y.S. Lee </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>d</span>Otolaryngology (Y.S.L.)</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=Y.S.%2BLee%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=Lee%20Y&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%3AY.S.%2BLee%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/0000-0001-6534-5753" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Y.S. Lee</a></li></ul></div><div class="author-tooltip-4"><div class="author-tooltip-name">J.Y. Hong </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>e</span>Oncology (J.Y.H.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.</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.Y.%2BHong%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=Hong%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.Y.%2BHong%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/0000-0003-1363-9332" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.Y. Hong</a></li></ul></div><div class="author-tooltip-5"><div class="author-tooltip-name">J.H. Lee </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Departments of Radiology and Research Institute of Radiology (Y.J.C., J.H.L., J.H.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=J.H.%2BLee%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=Lee%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.H.%2BLee%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/0000-0002-0021-4477" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.H. Lee</a></li></ul></div><div class="author-tooltip-6"><div class="author-tooltip-name">J.H. Baek </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Departments of Radiology and Research Institute of Radiology (Y.J.C., J.H.L., J.H.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=J.H.%2BBaek%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=Baek%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.H.%2BBaek%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/0000-0003-0480-4754" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for J.H. 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        	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-volume highwire-cite-metadata">Vol. 40</span>, <span  class="highwire-cite-metadata-issue highwire-cite-metadata">Issue 6 </span><div  class="highwire-cite-metadata-pubdate highwire-cite-metadata">1 Jun 2019 </div></div>
      
      
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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">Jun 2019, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1049-1054; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6066 </span></div>
  
  
  
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      <div class="highwire-cite-title">A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma</div>  
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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">Jun 2019, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">40 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(6) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">1049-1054; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A6066 </span></div>
  
  
  
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