Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography
AbstractThis study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negativ...
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| Veröffentlicht in: | Ultrasound in medicine & biology 2019-10, Vol.45 (10), p.2672-2678 |
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| description | AbstractThis study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist. |
| doi_str_mv | 10.1016/j.ultrasmedbio.2019.05.032 |
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Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.</description><identifier>ISSN: 0301-5629</identifier><identifier>EISSN: 1879-291X</identifier><identifier>DOI: 10.1016/j.ultrasmedbio.2019.05.032</identifier><identifier>PMID: 31262524</identifier><language>eng</language><publisher>England: Elsevier Inc</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Artificial intelligence ; Computer-aided diagnosis ; Diagnosis, Computer-Assisted ; Diagnosis, Differential ; Female ; Humans ; Image Interpretation, Computer-Assisted - methods ; Male ; Middle Aged ; Radiology ; Reproducibility of Results ; Retrospective Studies ; Sensitivity and Specificity ; Thyroid cancer ; Thyroid Gland - diagnostic imaging ; Thyroid nodule ; Thyroid Nodule - diagnostic imaging ; Ultrasonography ; Ultrasonography - methods ; Young Adult</subject><ispartof>Ultrasound in medicine & biology, 2019-10, Vol.45 (10), p.2672-2678</ispartof><rights>World Federation for Ultrasound in Medicine & Biology</rights><rights>2019 World Federation for Ultrasound in Medicine & Biology</rights><rights>Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c435t-ea38a839930507021bf3e5b2328407d205c82bc77724228b08d72b704c1e645b3</citedby><cites>FETCH-LOGICAL-c435t-ea38a839930507021bf3e5b2328407d205c82bc77724228b08d72b704c1e645b3</cites><orcidid>0000-0001-7752-5858</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ultrasmedbio.2019.05.032$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31262524$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Hye Lin</creatorcontrib><creatorcontrib>Ha, Eun Ju</creatorcontrib><creatorcontrib>Han, Miran</creatorcontrib><title>Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography</title><title>Ultrasound in medicine & biology</title><addtitle>Ultrasound Med Biol</addtitle><description>AbstractThis study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Artificial intelligence</subject><subject>Computer-aided diagnosis</subject><subject>Diagnosis, Computer-Assisted</subject><subject>Diagnosis, Differential</subject><subject>Female</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Sensitivity and Specificity</subject><subject>Thyroid cancer</subject><subject>Thyroid Gland - diagnostic imaging</subject><subject>Thyroid nodule</subject><subject>Thyroid Nodule - diagnostic imaging</subject><subject>Ultrasonography</subject><subject>Ultrasonography - methods</subject><subject>Young Adult</subject><issn>0301-5629</issn><issn>1879-291X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU1v1DAQhi0EotvCX0AWJy4JYzvOBwekaksBqQJEu6I3y7EnWy9JvLUTpPx7vGxBiBOnOfiZecfPEPKSQc6Ala93-dxPQccBbet8zoE1OcgcBH9EVqyumow37PYxWYEAlsmSNyfkNMYdAFSlqJ6SE8F4ySUvVsR8Rd1n33zoLf2CofNh0KNB6ju69sN-njBk586ipRdOb0cfXaTXS5xwoImlN3dL8M7ST97OPUa6iW7c0s2v9fzot0Hv75Zn5Emn-4jPH-oZ2Vy-u1l_yK4-v_-4Pr_KTCHklKEWta5F0wiQUAFnbSdQtlzwuoDKcpCm5q2pqooXnNct1LbibQWFYVgWshVn5NVx7j74-xnjpAYXDfa9HtHPUXEuGQORwhL65oia4GMM2Kl9cIMOi2KgDpLVTv0tWR0kK5AqSU7NLx5y5jY9_2n9bTUBF0cA029_OAwqGodJq3UBzaSsd_-X8_afMaZ3ozO6_44Lxp2fw5h8KqYiV6CuD-c-XJslg0nRrfgJ0p6pkg</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Kim, Hye Lin</creator><creator>Ha, Eun Ju</creator><creator>Han, Miran</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7752-5858</orcidid></search><sort><creationdate>20191001</creationdate><title>Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography</title><author>Kim, Hye Lin ; Ha, Eun Ju ; Han, Miran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c435t-ea38a839930507021bf3e5b2328407d205c82bc77724228b08d72b704c1e645b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Artificial intelligence</topic><topic>Computer-aided diagnosis</topic><topic>Diagnosis, Computer-Assisted</topic><topic>Diagnosis, Differential</topic><topic>Female</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Sensitivity and Specificity</topic><topic>Thyroid cancer</topic><topic>Thyroid Gland - diagnostic imaging</topic><topic>Thyroid nodule</topic><topic>Thyroid Nodule - diagnostic imaging</topic><topic>Ultrasonography</topic><topic>Ultrasonography - methods</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Hye Lin</creatorcontrib><creatorcontrib>Ha, Eun Ju</creatorcontrib><creatorcontrib>Han, Miran</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Ultrasound in medicine & biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Hye Lin</au><au>Ha, Eun Ju</au><au>Han, Miran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography</atitle><jtitle>Ultrasound in medicine & biology</jtitle><addtitle>Ultrasound Med Biol</addtitle><date>2019-10-01</date><risdate>2019</risdate><volume>45</volume><issue>10</issue><spage>2672</spage><epage>2678</epage><pages>2672-2678</pages><issn>0301-5629</issn><eissn>1879-291X</eissn><abstract>AbstractThis study evaluated the diagnostic performance of a commercially available computer-aided diagnosis (CAD) system (S-Detect 1 and S-Detect 2 for thyroid) for detecting thyroid cancers. Among 218 thyroid nodules in 106 patients, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the CAD systems were 80.2%, 82.6%, 75.0%, 86.3% and 81.7%, respectively, for the S-Detect 1 and 81.4%, 68.2%, 62.5%, 84.9% and 73.4%, respectively, for the S-Detect 2. The inter-observer agreement between the CAD system and radiologist for the description of calcifications was fair (kappa = 0.336), while the final diagnosis and each ultrasonographic descriptor showed moderate to substantial agreement for the S-Detect 2. To conclude, the current CAD systems had limited specificity in the diagnosis of thyroid cancer. One of the main limitations of the S-Detect 2 was its inaccuracy in recognizing calcifications, which meant that differentiation had to be undertaken by the radiologist.</abstract><cop>England</cop><pub>Elsevier Inc</pub><pmid>31262524</pmid><doi>10.1016/j.ultrasmedbio.2019.05.032</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-7752-5858</orcidid></addata></record> |
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| subjects | Adult Aged Aged, 80 and over Artificial intelligence Computer-aided diagnosis Diagnosis, Computer-Assisted Diagnosis, Differential Female Humans Image Interpretation, Computer-Assisted - methods Male Middle Aged Radiology Reproducibility of Results Retrospective Studies Sensitivity and Specificity Thyroid cancer Thyroid Gland - diagnostic imaging Thyroid nodule Thyroid Nodule - diagnostic imaging Ultrasonography Ultrasonography - methods Young Adult |
| title | Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography |
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