A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.
Identifieur interne : 000289 ( PubMed/Corpus ); précédent : 000288; suivant : 000290A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.
Auteurs : Jakob Unger ; Jörg Lohscheller ; Maximilian Reiter ; Katharina Eder ; Christian S. Betz ; Maria SchusterSource :
- Cancer research [ 1538-7445 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- diagnosis : Laryngeal Neoplasms, Precancerous Conditions.
- methods : Laryngoscopy.
- pathology : Laryngeal Neoplasms, Precancerous Conditions, Vocal Cords.
- Diagnosis, Differential, Female, Humans, Male, Middle Aged.
Abstract
About two thirds of laryngeal cancers originate at the vocal cords. Early-stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures was extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) revealed that vocal fold dynamics is significantly affected in the presence of precancerous lesions and T1a carcinoma. On the basis of the three measures, a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity, 100%; specificity, 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of noninvasive diagnostics of vocal fold lesions.
DOI: 10.1158/0008-5472.CAN-14-1458
PubMed: 25371410
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Betz</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Schuster, Maria" sort="Schuster, Maria" uniqKey="Schuster M" first="Maria" last="Schuster">Maria Schuster</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2015">2015</date><idno type="RBID">pubmed:25371410</idno><idno type="pmid">25371410</idno><idno type="doi">10.1158/0008-5472.CAN-14-1458</idno><idno type="wicri:Area/PubMed/Corpus">000289</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000289</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.</title><author><name sortKey="Unger, Jakob" sort="Unger, Jakob" uniqKey="Unger J" first="Jakob" last="Unger">Jakob Unger</name><affiliation><nlm:affiliation>Department of Computer Science, Trier University of Applied Sciences, Trier, Germany. unger@hochschule-trier.de.</nlm:affiliation></affiliation></author><author><name sortKey="Lohscheller, Jorg" sort="Lohscheller, Jorg" uniqKey="Lohscheller J" first="Jörg" last="Lohscheller">Jörg Lohscheller</name><affiliation><nlm:affiliation>Department of Computer Science, Trier University of Applied Sciences, Trier, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Reiter, Maximilian" sort="Reiter, Maximilian" uniqKey="Reiter M" first="Maximilian" last="Reiter">Maximilian Reiter</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Eder, Katharina" sort="Eder, Katharina" uniqKey="Eder K" first="Katharina" last="Eder">Katharina Eder</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Betz, Christian S" sort="Betz, Christian S" uniqKey="Betz C" first="Christian S" last="Betz">Christian S. Betz</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author><author><name sortKey="Schuster, Maria" sort="Schuster, Maria" uniqKey="Schuster M" first="Maria" last="Schuster">Maria Schuster</name><affiliation><nlm:affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Cancer research</title><idno type="eISSN">1538-7445</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Diagnosis, Differential</term><term>Female</term><term>Humans</term><term>Laryngeal Neoplasms (diagnosis)</term><term>Laryngeal Neoplasms (pathology)</term><term>Laryngoscopy (methods)</term><term>Male</term><term>Middle Aged</term><term>Precancerous Conditions (diagnosis)</term><term>Precancerous Conditions (pathology)</term><term>Vocal Cords (pathology)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Laryngeal Neoplasms</term><term>Precancerous Conditions</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Laryngoscopy</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Laryngeal Neoplasms</term><term>Precancerous Conditions</term><term>Vocal Cords</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Diagnosis, Differential</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">About two thirds of laryngeal cancers originate at the vocal cords. Early-stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures was extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) revealed that vocal fold dynamics is significantly affected in the presence of precancerous lesions and T1a carcinoma. On the basis of the three measures, a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity, 100%; specificity, 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of noninvasive diagnostics of vocal fold lesions.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25371410</PMID><DateCreated><Year>2015</Year><Month>01</Month><Day>06</Day></DateCreated><DateCompleted><Year>2015</Year><Month>04</Month><Day>23</Day></DateCompleted><DateRevised><Year>2015</Year><Month>01</Month><Day>06</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1538-7445</ISSN><JournalIssue CitedMedium="Internet"><Volume>75</Volume><Issue>1</Issue><PubDate><Year>2015</Year><Month>Jan</Month><Day>01</Day></PubDate></JournalIssue><Title>Cancer research</Title><ISOAbbreviation>Cancer Res.</ISOAbbreviation></Journal><ArticleTitle>A noninvasive procedure for early-stage discrimination of malignant and precancerous vocal fold lesions based on laryngeal dynamics analysis.</ArticleTitle><Pagination><MedlinePgn>31-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1158/0008-5472.CAN-14-1458</ELocationID><Abstract><AbstractText>About two thirds of laryngeal cancers originate at the vocal cords. Early-stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures was extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) revealed that vocal fold dynamics is significantly affected in the presence of precancerous lesions and T1a carcinoma. On the basis of the three measures, a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity, 100%; specificity, 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of noninvasive diagnostics of vocal fold lesions.</AbstractText><CopyrightInformation>©2014 American Association for Cancer Research.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Unger</LastName><ForeName>Jakob</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Trier University of Applied Sciences, Trier, Germany. unger@hochschule-trier.de.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lohscheller</LastName><ForeName>Jörg</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Trier University of Applied Sciences, Trier, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Reiter</LastName><ForeName>Maximilian</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Eder</LastName><ForeName>Katharina</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Betz</LastName><ForeName>Christian S</ForeName><Initials>CS</Initials><AffiliationInfo><Affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schuster</LastName><ForeName>Maria</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Otorhinolaryngology, Head and Neck Surgery, University of Munich, Munich, Germany.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>11</Month><Day>04</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cancer Res</MedlineTA><NlmUniqueID>2984705R</NlmUniqueID><ISSNLinking>0008-5472</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003937" MajorTopicYN="N">Diagnosis, Differential</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007822" MajorTopicYN="N">Laryngeal Neoplasms</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007828" MajorTopicYN="N">Laryngoscopy</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011230" MajorTopicYN="N">Precancerous Conditions</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014827" MajorTopicYN="N">Vocal Cords</DescriptorName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>11</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>11</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>4</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25371410</ArticleId><ArticleId IdType="pii">0008-5472.CAN-14-1458</ArticleId><ArticleId IdType="doi">10.1158/0008-5472.CAN-14-1458</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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