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Grayscale Ultrasound Radiomic Features and Shear-Wave Elastography Radiomic Features in Benign and Malignant Breast Masses

 Ji Hyun Youk  ;  Jin Young Kwak  ;  Eunjung Lee  ;  Eun Ju Son  ;  Jeong-Ah Kim 
 ULTRASCHALL IN DER MEDIZIN, Vol.41(4) : 390-396, 2020-08 
Journal Title
Issue Date
Breast Neoplasms* / diagnostic imaging ; Diagnosis, Differential ; Elasticity Imaging Techniques* ; Female ; Humans ; Reproducibility of Results ; Retrospective Studies ; Sensitivity and Specificity ; Ultrasonography, Mammary*
Purpose: To identify and compare diagnostic performance of radiomic features between grayscale ultrasound (US) and shear-wave elastography (SWE) in breast masses.

Materials and methods: We retrospectively collected 328 pathologically confirmed breast masses in 296 women who underwent grayscale US and SWE before biopsy or surgery. A representative SWE image of the mass displayed with a grayscale image in split-screen mode was selected. An ROI was delineated around the mass boundary on the grayscale image and copied and pasted to the SWE image by a dedicated breast radiologist for lesion segmentation. A total of 730 candidate radiomic features including first-order statistics and textural and wavelet features were extracted from each image. LASSO regression was used for data dimension reduction and feature selection. Univariate and multivariate logistic regression was performed to identify independent radiomic features, differentiating between benign and malignant masses with calculation of the AUC.

Results: Of 328 breast masses, 205 (62.5 %) were benign and 123 (37.5 %) were malignant. Following radiomic feature selection, 22 features from grayscale and 6 features from SWE remained. On univariate analysis, all 6 SWE radiomic features (P < 0.0001) and 21 of 22 grayscale radiomic features (P < 0.03) were significantly different between benign and malignant masses. After multivariate analysis, three grayscale radiomic features and two SWE radiomic features were independently associated with malignant breast masses. The AUC was 0.929 for grayscale US and 0.992 for SWE (P < 0.001).

Conclusion: US radiomic features may have the potential to improve diagnostic performance for breast masses, but further investigation of independent and larger datasets is needed.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kwak, Jin Young(곽진영) ORCID logo https://orcid.org/0000-0002-6212-1495
Kim, Jeong Ah(김정아) ORCID logo https://orcid.org/0000-0003-4949-4913
Son, Eun Ju(손은주) ORCID logo https://orcid.org/0000-0002-7895-0335
Youk, Ji Hyun(육지현) ORCID logo https://orcid.org/0000-0002-7787-780X
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