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MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer

DC Field Value Language
dc.contributor.author윤정현-
dc.contributor.author박영진-
dc.contributor.author김성원-
dc.contributor.author김민정-
dc.contributor.author김은경-
dc.date.accessioned2020-09-28T00:57:35Z-
dc.date.available2020-09-28T00:57:35Z-
dc.date.issued2020-02-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/178999-
dc.description.abstractRadiomic features hold potential to improve prediction of disease-free survival (DFS) in triple-negative breast cancer (TNBC) and may show better performance if developed from TNBC patients. We aimed to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. A total of 228 TNBC patients who underwent preoperative MRI and surgery between April 2012 and December 2016 were included. Patients were temporally divided into the training (n = 169) and validation (n = 59) set. Radiomic features of the tumor were extracted from T2-weighted and contrast-enhanced T1- weighted MRI. Then a radiomics score was constructed with the least absolute shrinkage and selection operator regression in the training set. Univariate and multivariate Cox proportional hazards models were used to determine what associations the radiomics score and clinicopathologic variables had with DFS. A combined clinicopathologic-radiomic (CCR) model was constructed based on multivariate Cox analysis. The incremental values of the radiomics score were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC) and bootstrapping (n = 1000). The radiomics score, which consisted of 5 selected MRI features, was significantly associated with worse DFS in both the training and validation sets (p = 0.002, p = 0.033, respectively). In both the training and validation set, the radiomics score showed comparable performance with the clinicopathologic model. The CCR model demonstrated better performance than the clinicopathologic model in the training set (iAUC, 0.844; difference in iAUC, p < 0.001) and validation set (iAUC, 0.765, difference in iAUC, p < 0.001). In conclusion, MRI-based radiomic features can improve the prediction of DFS when integrated with clinicopathologic data in patients with TNBC.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorSungwon Kim-
dc.contributor.googleauthorMin Jung Kim-
dc.contributor.googleauthorEun-Kyung Kim-
dc.contributor.googleauthorJung Hyun Yoon-
dc.contributor.googleauthorVivian Youngjean Park-
dc.identifier.doi10.1038/s41598-020-60822-9-
dc.contributor.localIdA02595-
dc.contributor.localIdA01572-
dc.contributor.localIdA05309-
dc.contributor.localIdA00473-
dc.contributor.localIdA00801-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid32111957-
dc.contributor.alternativeNameYoon, Jung Hyun-
dc.contributor.affiliatedAuthor윤정현-
dc.contributor.affiliatedAuthor박영진-
dc.contributor.affiliatedAuthor김성원-
dc.contributor.affiliatedAuthor김민정-
dc.contributor.affiliatedAuthor김은경-
dc.citation.volume10-
dc.citation.number1-
dc.citation.startPage3750-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.10(1) : 3750, 2020-02-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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