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Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach

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dc.contributor.author김진아-
dc.contributor.author손범석-
dc.contributor.author안성수-
dc.contributor.author양재문-
dc.contributor.author이승구-
dc.contributor.author최윤성-
dc.date.accessioned2020-12-01T17:05:55Z-
dc.date.available2020-12-01T17:05:55Z-
dc.date.issued2020-10-
dc.identifier.issn0513-5796-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/180121-
dc.description.abstractThe purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613-0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467-0.759) and 0.663 (95% CI, 0.531-0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherYonsei University-
dc.relation.isPartOfYONSEI MEDICAL JOURNAL-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAlgorithms-
dc.subject.MESHBiopsy-
dc.subject.MESHCarcinoma, Squamous Cell / diagnostic imaging*-
dc.subject.MESHCarcinoma, Squamous Cell / pathology-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHImage Enhancement / methods-
dc.subject.MESHLymphoma / diagnostic imaging*-
dc.subject.MESHLymphoma / pathology-
dc.subject.MESHMachine Learning-
dc.subject.MESHMagnetic Resonance Imaging / methods*-
dc.subject.MESHOropharyngeal Neoplasms / diagnostic imaging*-
dc.subject.MESHOropharyngeal Neoplasms / pathology-
dc.subject.MESHOropharynx / diagnostic imaging*-
dc.subject.MESHOropharynx / pathology-
dc.subject.MESHROC Curve-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHSensitivity and Specificity-
dc.subject.MESHTreatment Outcome-
dc.titleSquamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSohi Bae-
dc.contributor.googleauthorYoon Seong Choi-
dc.contributor.googleauthorBeomseok Sohn-
dc.contributor.googleauthorSung Soo Ahn-
dc.contributor.googleauthorSeung Koo Lee-
dc.contributor.googleauthorJaemoon Yang-
dc.contributor.googleauthorJinna Kim-
dc.identifier.doi10.3349/ymj.2020.61.10.895-
dc.contributor.localIdA01022-
dc.contributor.localIdA04960-
dc.contributor.localIdA02234-
dc.contributor.localIdA02315-
dc.contributor.localIdA02912-
dc.contributor.localIdA04137-
dc.relation.journalcodeJ02813-
dc.identifier.eissn1976-2437-
dc.identifier.pmid32975065-
dc.subject.keywordRadiomics-
dc.subject.keywordlymphoma-
dc.subject.keywordmagnetic resonance imaging-
dc.subject.keywordoropharynx-
dc.subject.keywordsquamous cell carcinoma-
dc.contributor.alternativeNameKim, Jinna-
dc.contributor.affiliatedAuthor김진아-
dc.contributor.affiliatedAuthor손범석-
dc.contributor.affiliatedAuthor안성수-
dc.contributor.affiliatedAuthor양재문-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor최윤성-
dc.citation.volume61-
dc.citation.number10-
dc.citation.startPage895-
dc.citation.endPage900-
dc.identifier.bibliographicCitationYONSEI MEDICAL JOURNAL, Vol.61(10) : 895-900, 2020-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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