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Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

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dc.contributor.author육지현-
dc.contributor.author신일아-
dc.contributor.author문희정-
dc.contributor.author곽진영-
dc.date.accessioned2020-09-29T01:45:42Z-
dc.date.available2020-09-29T01:45:42Z-
dc.date.issued2020-07-
dc.identifier.issn2288-5919-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/179498-
dc.description.abstractPurpose: This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). Methods: In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. Results: In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. Conclusion: Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherKorean Society of Ultrasound in Medicine-
dc.relation.isPartOfULTRASONOGRAPHY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleApplication of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorIlah Shin-
dc.contributor.googleauthorYoung Jae Kim-
dc.contributor.googleauthorKyunghwa Han-
dc.contributor.googleauthorEunjung Lee-
dc.contributor.googleauthorHye Jung Kim-
dc.contributor.googleauthorJung Hee Shin-
dc.contributor.googleauthorHee Jung Moon-
dc.contributor.googleauthorJi Hyun Youk-
dc.contributor.googleauthorKwang Gi Kim-
dc.contributor.googleauthorJin Young Kwak-
dc.identifier.doi10.14366/usg.19069-
dc.contributor.localIdA02537-
dc.contributor.localIdA05848-
dc.contributor.localIdA01397-
dc.contributor.localIdA00182-
dc.relation.journalcodeJ02768-
dc.identifier.eissn2288-5943-
dc.identifier.pmid32299197-
dc.subject.keywordArtificial neural network-
dc.subject.keywordFollicular neoplasm-
dc.subject.keywordMachine learning-
dc.subject.keywordSupport vector machine-
dc.subject.keywordUltrasonography-
dc.contributor.alternativeNameYouk, Ji Hyun-
dc.contributor.affiliatedAuthor육지현-
dc.contributor.affiliatedAuthor신일아-
dc.contributor.affiliatedAuthor문희정-
dc.contributor.affiliatedAuthor곽진영-
dc.citation.volume39-
dc.citation.number3-
dc.citation.startPage257-
dc.citation.endPage265-
dc.identifier.bibliographicCitationULTRASONOGRAPHY, Vol.39(3) : 257-265, 2020-07-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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