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<meta name="DC.Title" content="Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies" />
<meta name="DC.Identifier" content="10.3174/ajnr.A1901" />
<meta name="DC.Date" content="2010-04-01" />
<meta name="DC.Publisher" content="American Journal of Neuroradiology" />
<meta name="DC.Rights" content="Copyright © American Society of Neuroradiology. 
Indicates open access to non-subscribers at www.ajnr.org" />
<meta name="DC.AccessRights" content="open-access" />
<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies.

MATERIAL AND METHODS: MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADCL; high, ADC-ADCH) and 3 partitions (ADCL; intermediate, ADC-ADCI; ADCH) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies.

RESULTS: A statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers ( P = .03 and .022, respectively) and the 2-cluster model of reader 2 ( P = .04), with the other metrics (ADCH, ADCI; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADCH and ADCL in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825).

CONCLUSIONS: The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

ADC
:   apparent diffusion coefficient
DWI
:   diffusion-weighted imaging
FA
:   flip angle
ROC
:   receiver operator characteristic
SCC
:   squamous cell carcinoma
SNUC
:   sinonasal undifferentiated carcinoma
VOI
:   volume of interest" />
<meta name="DC.Contributor" content="A. Srinivasan" />
<meta name="DC.Contributor" content="C.J. Galbán" />
<meta name="DC.Contributor" content="T.D. Johnson" />
<meta name="DC.Contributor" content="T.L. Chenevert" />
<meta name="DC.Contributor" content="B.D. Ross" />
<meta name="DC.Contributor" content="S.K. Mukherji" />
<meta name="article:published_time" content="2010-04-01" />
<meta name="article:section" content="Head and Neck" />
<meta name="citation_title" content="Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies.&lt;/p&gt;&lt;h3&gt;MATERIAL AND METHODS:&lt;/h3&gt;
&lt;p&gt;MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC&lt;sub&gt;L&lt;/sub&gt;; high, ADC-ADC&lt;sub&gt;H&lt;/sub&gt;) and 3 partitions (ADC&lt;sub&gt;L&lt;/sub&gt;; intermediate, ADC-ADC&lt;sub&gt;I&lt;/sub&gt;; ADC&lt;sub&gt;H&lt;/sub&gt;) by using the K-means clustering algorithm. An unpaired 2-tailed Student &lt;i&gt;t&lt;/i&gt; test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;A statistically significant difference between the mean ADC&lt;sub&gt;L&lt;/sub&gt; clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (&lt;i&gt;P&lt;/i&gt; = .03 and .022, respectively) and the 2-cluster model of reader 2 (&lt;i&gt;P&lt;/i&gt; = .04), with the other metrics (ADC&lt;sub&gt;H&lt;/sub&gt;, ADC&lt;sub&gt;I&lt;/sub&gt;; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC&lt;sub&gt;H&lt;/sub&gt; and ADC&lt;sub&gt;L&lt;/sub&gt; in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: &lt;i&gt;P&lt;/i&gt; = .008, area under curve = 0.850; 3 clusters: &lt;i&gt;P&lt;/i&gt; = .01, area under curve = 0.825).&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.&lt;/p&gt;" />
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<meta name="twitter:title" content="Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies" />
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<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies.

MATERIAL AND METHODS: MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADCL; high, ADC-ADCH) and 3 partitions (ADCL; intermediate, ADC-ADCI; ADCH) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies.

RESULTS: A statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers ( P = .03 and .022, respectively) and the 2-cluster model of reader 2 ( P = .04), with the other metrics (ADCH, ADCI; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADCH and ADCL in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825).

CONCLUSIONS: The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

ADC
:   apparent diffusion coefficient
DWI
:   diffusion-weighted imaging
FA
:   flip angle
ROC
:   receiver operator characteristic
SCC
:   squamous cell carcinoma
SNUC
:   sinonasal undifferentiated carcinoma
VOI
:   volume of interest" />
<meta name="og-title" property="og:title" content="Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies" />
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<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies.

MATERIAL AND METHODS: MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADCL; high, ADC-ADCH) and 3 partitions (ADCL; intermediate, ADC-ADCI; ADCH) by using the K-means clustering algorithm. An unpaired 2-tailed Student t test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies.

RESULTS: A statistically significant difference between the mean ADCL clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers ( P = .03 and .022, respectively) and the 2-cluster model of reader 2 ( P = .04), with the other metrics (ADCH, ADCI; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADCH and ADCL in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: P = .008, area under curve = 0.850; 3 clusters: P = .01, area under curve = 0.825).

CONCLUSIONS: The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.

ADC
:   apparent diffusion coefficient
DWI
:   diffusion-weighted imaging
FA
:   flip angle
ROC
:   receiver operator characteristic
SCC
:   squamous cell carcinoma
SNUC
:   sinonasal undifferentiated carcinoma
VOI
:   volume of interest" />
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    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0">A. Srinivasan</span>, <span class="highwire-citation-author" data-delta="1">C.J. Galbán</span>, <span class="highwire-citation-author" data-delta="2">T.D. Johnson</span>, <span class="highwire-citation-author" data-delta="3">T.L. Chenevert</span>, <span class="highwire-citation-author" data-delta="4">B.D. Ross</span> and <span class="highwire-citation-author" data-delta="5">S.K. Mukherji</span></span></div>
  
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<div id="hw-article-author-popups-top-node-9854--6919393499" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">A. Srinivasan </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.%2BSrinivasan%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=Srinivasan%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.%2BSrinivasan%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">C.J. 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<p id="p-4"><strong>BACKGROUND AND PURPOSE:</strong> Does the K-means algorithm do a better job of differentiating benign and malignant neck pathologies compared to only mean ADC? The objective of our study was to analyze the differences between ADC partitions to evaluate whether the K-means technique can be of additional benefit to whole-lesion mean ADC alone in distinguishing benign and malignant neck pathologies.</p>
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<p id="p-5"><strong>MATERIAL AND METHODS:</strong> MR imaging studies of 10 benign and 10 malignant proved neck pathologies were postprocessed on a PC by using in-house software developed in Matlab. Two neuroradiologists manually contoured the lesions, with the ADC values within each lesion clustered into 2 (low, ADC-ADC<sub>L</sub>; high, ADC-ADC<sub>H</sub>) and 3 partitions (ADC<sub>L</sub>; intermediate, ADC-ADC<sub>I</sub>; ADC<sub>H</sub>) by using the K-means clustering algorithm. An unpaired 2-tailed Student <em>t</em> test was performed for all metrics to determine statistical differences in the means of the benign and malignant pathologies.</p>
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<p id="p-6"><strong>RESULTS:</strong> A statistically significant difference between the mean ADC<sub>L</sub> clusters in benign and malignant pathologies was seen in the 3-cluster models of both readers (<em>P</em> = .03 and .022, respectively) and the 2-cluster model of reader 2 (<em>P</em> = .04), with the other metrics (ADC<sub>H</sub>, ADC<sub>I</sub>; whole-lesion mean ADC) not revealing any significant differences. ROC curves demonstrated the quantitative differences in mean ADC<sub>H</sub> and ADC<sub>L</sub> in both the 2- and 3-cluster models to be predictive of malignancy (2 clusters: <em>P</em> = .008, area under curve = 0.850; 3 clusters: <em>P</em> = .01, area under curve = 0.825).</p>
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<p id="p-7"><strong>CONCLUSIONS:</strong> The K-means clustering algorithm that generates partitions of large datasets may provide a better characterization of neck pathologies and may be of additional benefit in distinguishing benign and malignant neck pathologies compared with whole-lesion mean ADC alone.</p>
</div></div><div class="section glossary" id="glossary-1"><h2>Abbreviations</h2><dl id="def-list-1"><dt id="def-item-1">ADC<span class="def-item-dt-sep"></span></dt><dd>apparent diffusion coefficient<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">DWI<span class="def-item-dt-sep"></span></dt><dd>diffusion-weighted imaging<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">FA<span class="def-item-dt-sep"></span></dt><dd>flip angle<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">ROC<span class="def-item-dt-sep"></span></dt><dd>receiver operator characteristic<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">SCC<span class="def-item-dt-sep"></span></dt><dd>squamous cell carcinoma<span class="def-item-dd-sep"></span></dd><dt id="def-item-6">SNUC<span class="def-item-dt-sep"></span></dt><dd>sinonasal undifferentiated carcinoma<span class="def-item-dd-sep"></span></dd><dt id="def-item-7">VOI<span class="def-item-dt-sep"></span></dt><dd>volume of interest</dd></dl></div><ul class="copyright-statement"><li class="fn" id="copyright-statement-1">Copyright © American Society of Neuroradiology</li></ul><div class="license" id="license-1"><p id="p-3">Indicates open access to non-subscribers at <a href="http://www.ajnr.org">www.ajnr.org</a></p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div><a href="http://ajnr-stage2.stage.highwire.org/content/31/4/736.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-title">Utility of the K-Means Clustering Algorithm in Differentiating Apparent Diffusion Coefficient Values of Benign and Malignant Neck Pathologies</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">A.</span> <span class="nlm-surname">Srinivasan</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">C.J.</span> <span class="nlm-surname">Galbán</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">T.D.</span> <span class="nlm-surname">Johnson</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">T.L.</span> <span class="nlm-surname">Chenevert</span></span>, <span class="highwire-citation-author" data-delta="4"><span class="nlm-given-names">B.D.</span> <span class="nlm-surname">Ross</span></span>, <span class="highwire-citation-author" data-delta="5"><span class="nlm-given-names">S.K.</span> <span class="nlm-surname">Mukherji</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-oa-ind highwire-cite-metadata"><i class="highwire-oa-indicator"></i> </span><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Apr 2010, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">31 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(4) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">736-740; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A1901 </span></div>
  
  
  
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