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

Authors
 Sungwon Kim  ;  Min Jung Kim  ;  Eun-Kyung Kim  ;  Jung Hyun Yoon  ;  Vivian Youngjean Park 
Citation
 SCIENTIFIC REPORTS, Vol.10(1) : 3750, 2020-02 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2020-02
Abstract
Radiomic 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.
Files in This Item:
T202002435.pdf Download
DOI
10.1038/s41598-020-60822-9
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min Jung(김민정) ORCID logo https://orcid.org/0000-0003-4949-1237
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Park, Vivian Youngjean(박영진) ORCID logo https://orcid.org/0000-0002-5135-4058
Yoon, Jung Hyun(윤정현) ORCID logo https://orcid.org/0000-0002-2100-3513
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/178999
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