Squamous Cell Carcinoma and Lymphoma of the oropharynx: Differentiation Using a Radiomics Approach
DC Field | Value | Language |
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dc.contributor.author | Bae, Sohi | - |
dc.contributor.author | Choi, Yoon Seong | - |
dc.contributor.author | Sohn, Beomseok | - |
dc.contributor.author | Ahn, Sung Soo | - |
dc.contributor.author | Lee, Seung-Koo | - |
dc.contributor.author | Yang, Jaemoon | - |
dc.contributor.author | Kim, Jinna | - |
dc.date.accessioned | 2023-09-04T19:12:10Z | - |
dc.date.available | 2023-09-04T19:12:10Z | - |
dc.date.created | 2023-07-19 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 0513-5796 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190143 | - |
dc.description.abstract | The 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 dassifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics dassifier 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 dassifier 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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | YONSEI UNIV COLL MEDICINE | - |
dc.title | Squamous Cell Carcinoma and Lymphoma of the oropharynx: Differentiation Using a Radiomics Approach | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Bae, Sohi | - |
dc.identifier.doi | 10.3349/ymj.2020.61.10.895 | - |
dc.identifier.scopusid | 2-s2.0-85091620824 | - |
dc.identifier.wosid | 000576629500009 | - |
dc.identifier.bibliographicCitation | YONSEI MEDICAL JOURNAL, v.61, no.10, pp.895 - 900 | - |
dc.relation.isPartOf | YONSEI MEDICAL JOURNAL | - |
dc.citation.title | YONSEI MEDICAL JOURNAL | - |
dc.citation.volume | 61 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 895 | - |
dc.citation.endPage | 900 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.identifier.kciid | ART002626535 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordPlus | APPARENT DIFFUSION-COEFFICIENTS | - |
dc.subject.keywordPlus | CONTRAST-ENHANCED MRI | - |
dc.subject.keywordPlus | MALIGNANT-LYMPHOMA | - |
dc.subject.keywordPlus | TEXTURE ANALYSIS | - |
dc.subject.keywordPlus | HEAD | - |
dc.subject.keywordPlus | NECK | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | CT | - |
dc.subject.keywordAuthor | Lymphoma | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Oropharynx | - |
dc.subject.keywordAuthor | Radiomics | - |
dc.subject.keywordAuthor | Squamous cell carcinoma | - |
dc.identifier.url | https://eymj.org/DOIx.php?id=10.3349/ymj.2020.61.10.895 | - |
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