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Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
DC Field | Value | Language |
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dc.contributor.author | 김성원 | - |
dc.contributor.author | 김은경 | - |
dc.contributor.author | 이시은 | - |
dc.date.accessioned | 2021-01-19T07:51:08Z | - |
dc.date.available | 2021-01-19T07:51:08Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/181349 | - |
dc.description.abstract | We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Jinwoo Son | - |
dc.contributor.googleauthor | Si Eun Lee | - |
dc.contributor.googleauthor | Eun-Kyung Kim | - |
dc.contributor.googleauthor | Sungwon Kim | - |
dc.identifier.doi | 10.1038/s41598-020-78681-9 | - |
dc.contributor.localId | A05309 | - |
dc.contributor.localId | A00801 | - |
dc.contributor.localId | A05611 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 33299040 | - |
dc.contributor.alternativeName | Kim, Sungwon | - |
dc.contributor.affiliatedAuthor | 김성원 | - |
dc.contributor.affiliatedAuthor | 김은경 | - |
dc.contributor.affiliatedAuthor | 이시은 | - |
dc.citation.volume | 10 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 21566 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.10(1) : 21566, 2020-12 | - |
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