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Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

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dc.contributor.authorLee, Si Eun-
dc.contributor.authorSim, Yongsik-
dc.contributor.authorKim, Sungwon-
dc.contributor.authorKim, Eun-Kyung-
dc.contributor.author심용식-
dc.date.accessioned2021-03-31T01:59:59Z-
dc.date.available2021-03-31T01:59:59Z-
dc.date.created2021-02-22-
dc.date.issued2021-01-
dc.identifier.issn2288-5919-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181859-
dc.description.abstractPurpose: The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods: A total of 496 patients (mean age, 52.5 +/- 10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. Results: In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. Conclusion: A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.-
dc.formatapplication/pdf-
dc.language영어-
dc.publisherKOREAN SOC ULTRASOUND MEDICINE-
dc.relation.isPartOfULTRASONOGRAPHY-
dc.titlePredictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer-
dc.typeArticle-
dc.contributor.googleauthorLee, Si Eun-
dc.contributor.googleauthorSim, Yongsik-
dc.contributor.googleauthorKim, Sungwon-
dc.contributor.googleauthorKim, Eun-Kyung-
dc.identifier.doi10.14366/usg.20026-
dc.relation.journalcodeJ02768-
dc.identifier.eissn2288-5943-
dc.subject.keywordBreast neoplasms-
dc.subject.keywordLymph nodes-
dc.subject.keywordUltrasonography-
dc.subject.keywordComputer-aided design-
dc.subject.keywordPreoperative period-
dc.contributor.affiliatedAuthorLee, Si Eun-
dc.contributor.affiliatedAuthorSim, Yongsik-
dc.contributor.affiliatedAuthorKim, Sungwon-
dc.contributor.affiliatedAuthorKim, Eun-Kyung-
dc.identifier.scopusid2-s2.0-85099294013-
dc.identifier.wosid000602774600010-
dc.citation.titleULTRASONOGRAPHY-
dc.citation.volume40-
dc.citation.number1-
dc.citation.startPage93-
dc.citation.endPage102-
dc.identifier.bibliographicCitationULTRASONOGRAPHY, Vol.40(1) : 93-102, 2021-01-
dc.identifier.rimsid67611-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorBreast neoplasms-
dc.subject.keywordAuthorLymph nodes-
dc.subject.keywordAuthorUltrasonography-
dc.subject.keywordAuthorComputer-aided design-
dc.subject.keywordAuthorPreoperative period-
dc.subject.keywordPlusCOMPUTER-AIDED PREDICTION-
dc.subject.keywordPlusSENTINEL NODE-
dc.subject.keywordPlusBIOPSY-
dc.subject.keywordPlusULTRASOUND-
dc.subject.keywordPlusNOMOGRAM-
dc.subject.keywordPlusDISSECTION-
dc.subject.keywordPlusONCOLOGY-
dc.subject.keywordPlusFEATURES-
dc.type.docTypeArticle-
dc.identifier.kciidART002669401-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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