Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma
To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC). From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, val...
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| description | To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC).
From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC |
| doi_str_mv | 10.1371/journal.pone.0228968 |
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From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109).
Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively.
Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0228968</identifier><identifier>PMID: 32053670</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Biology and Life Sciences ; Biomarkers ; Cancer ; Feature extraction ; Hospitals ; Lymphatic system ; Medical diagnosis ; Medical prognosis ; Medical schools ; Medicine ; Medicine and Health Sciences ; Metastasis ; Mortality ; Mutation ; Papillary thyroid carcinoma ; Patients ; Physical Sciences ; Radiology ; Radiomics ; Regression analysis ; Regression models ; Research and Analysis Methods ; Statistical analysis ; Subgroups ; Surgery ; Thyroid ; Thyroid cancer ; Training ; Ultrasound</subject><ispartof>PloS one, 2020, Vol.15 (2), p.e0228968-e0228968</ispartof><rights>2020 Yoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Yoon et al 2020 Yoon et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3718-60a08a636ba44ea2a5d0ef72e5ffab9188bb1f8140269127a2d7b7344cbe67b93</citedby><cites>FETCH-LOGICAL-c3718-60a08a636ba44ea2a5d0ef72e5ffab9188bb1f8140269127a2d7b7344cbe67b93</cites><orcidid>0000-0002-6212-1495</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018006/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018006/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,4010,23845,27900,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32053670$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yoon, Jung Hyun</creatorcontrib><creatorcontrib>Han, Kyunghwa</creatorcontrib><creatorcontrib>Lee, Eunjung</creatorcontrib><creatorcontrib>Lee, Jandee</creatorcontrib><creatorcontrib>Kim, Eun-Kyung</creatorcontrib><creatorcontrib>Moon, Hee Jung</creatorcontrib><creatorcontrib>Park, Vivian Youngjean</creatorcontrib><creatorcontrib>Nam, Kee Hyun</creatorcontrib><creatorcontrib>Kwak, Jin Young</creatorcontrib><title>Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC).
From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109).
Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively.
Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size.</description><subject>Age</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Feature extraction</subject><subject>Hospitals</subject><subject>Lymphatic system</subject><subject>Medical diagnosis</subject><subject>Medical prognosis</subject><subject>Medical schools</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Metastasis</subject><subject>Mortality</subject><subject>Mutation</subject><subject>Papillary thyroid carcinoma</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Statistical analysis</subject><subject>Subgroups</subject><subject>Surgery</subject><subject>Thyroid</subject><subject>Thyroid 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in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma</title><author>Yoon, Jung Hyun ; Han, Kyunghwa ; Lee, Eunjung ; Lee, Jandee ; Kim, Eun-Kyung ; Moon, Hee Jung ; Park, Vivian Youngjean ; Nam, Kee Hyun ; Kwak, Jin Young</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3718-60a08a636ba44ea2a5d0ef72e5ffab9188bb1f8140269127a2d7b7344cbe67b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Feature extraction</topic><topic>Hospitals</topic><topic>Lymphatic system</topic><topic>Medical diagnosis</topic><topic>Medical prognosis</topic><topic>Medical schools</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Metastasis</topic><topic>Mortality</topic><topic>Mutation</topic><topic>Papillary thyroid carcinoma</topic><topic>Patients</topic><topic>Physical Sciences</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Research and Analysis Methods</topic><topic>Statistical analysis</topic><topic>Subgroups</topic><topic>Surgery</topic><topic>Thyroid</topic><topic>Thyroid cancer</topic><topic>Training</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yoon, Jung Hyun</creatorcontrib><creatorcontrib>Han, Kyunghwa</creatorcontrib><creatorcontrib>Lee, Eunjung</creatorcontrib><creatorcontrib>Lee, Jandee</creatorcontrib><creatorcontrib>Kim, Eun-Kyung</creatorcontrib><creatorcontrib>Moon, Hee Jung</creatorcontrib><creatorcontrib>Park, Vivian Youngjean</creatorcontrib><creatorcontrib>Nam, Kee Hyun</creatorcontrib><creatorcontrib>Kwak, Jin 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Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yoon, Jung Hyun</au><au>Han, Kyunghwa</au><au>Lee, Eunjung</au><au>Lee, Jandee</au><au>Kim, Eun-Kyung</au><au>Moon, Hee Jung</au><au>Park, Vivian Youngjean</au><au>Nam, Kee Hyun</au><au>Kwak, Jin Young</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020</date><risdate>2020</risdate><volume>15</volume><issue>2</issue><spage>e0228968</spage><epage>e0228968</epage><pages>e0228968-e0228968</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC).
From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAFV600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10-85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAFV600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109).
Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAFV600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAFV600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516-0.742) to 0.718 (95% CI: 0.650-0.786), and 0.567 (95% CI: 0.434-0.699) to 0.729 (95% CI: 0.632-0.826), respectively.
Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAFV600E mutation status of PTC regardless of size.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32053670</pmid><doi>10.1371/journal.pone.0228968</doi><orcidid>https://orcid.org/0000-0002-6212-1495</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Biology and Life Sciences Biomarkers Cancer Feature extraction Hospitals Lymphatic system Medical diagnosis Medical prognosis Medical schools Medicine Medicine and Health Sciences Metastasis Mortality Mutation Papillary thyroid carcinoma Patients Physical Sciences Radiology Radiomics Regression analysis Regression models Research and Analysis Methods Statistical analysis Subgroups Surgery Thyroid Thyroid cancer Training Ultrasound |
| title | Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E mutations in papillary thyroid carcinoma |
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