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A Radiomics Approach for the Classification of Fibroepithelial Lesions on Breast Ultrasonography

Authors
 Yongsik Sim  ;  Si Eun Lee  ;  Eun-Kyung Kim  ;  Sungwon Kim 
Citation
 ULTRASOUND IN MEDICINE AND BIOLOGY, Vol.46(5) : 1133-1141, 2020-05 
Journal Title
 ULTRASOUND IN MEDICINE AND BIOLOGY 
ISSN
 0301-5629 
Issue Date
2020-05
Keywords
Breast ultrasonography ; Classification ; Feature selection ; Fibroepithelial lesion ; Radiomics
Abstract
A radiomics-based classifier to distinguish phyllodes tumor and fibroadenoma on gray-scale breast ultrasonography was developed and validated. A total of 93 radiomics features were extracted from representative transverse plane ultrasound images of 182 fibroepithelial lesions initially diagnosed by core needle biopsy. High-throughput radiomics features were selected using the intra-class correlation coefficient between two radiologist readers and the Least Absolute Shrinkage and Selection Operator regression through 10-fold cross-validation. When applied to the validation set, the radiomics classifier for the differentiation of phyllodes tumors and benign/fibroadenomas achieved an area under the receiver operating characteristic curve of 0.765 (95% confidence interval [CI]: 0.597-0.888) with an accuracy of 0.703 (sensitivity: 0.857; specificity: 0.5). Our radiomics signature-based classifier may help predict phyllodes tumors among fibroepithelial lesions on breast ultrasonography.
Full Text
https://www.sciencedirect.com/science/article/pii/S0301562920300417
DOI
10.1016/j.ultrasmedbio.2020.01.015
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
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
Lee, Si Eun(이시은) ORCID logo https://orcid.org/0000-0002-3225-5484
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176177
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