Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

Xiang Zhang; Ming Liang; Zehong Yang; Chushan Zheng; Jiayi Wu; Bing Ou; Haojiang Li; Xiaoyan Wu; Baoming Luo; Jun Shen

doi:10.3389/fonc.2020.01621

Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification

Front Oncol. 2020 Aug 28:10:1621. doi: 10.3389/fonc.2020.01621. eCollection 2020.

Authors

Xiang Zhang^{1

2}, Ming Liang^{2

3}, Zehong Yang^{1

2}, Chushan Zheng^{1

2}, Jiayi Wu^{2

3}, Bing Ou^{2

3}, Haojiang Li⁴, Xiaoyan Wu^{1

2}, Baoming Luo^{2

3}, Jun Shen^{1

2}

Affiliations

¹ Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
² Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
³ Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
⁴ Department of Radiology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China.

Abstract

Objective: Shear-wave elastography (SWE) can improve the diagnostic specificity of the B-model ultrasonography (US) in breast cancer. However, whether deep learning-based radiomics signatures based on the B-mode US (B-US-RS) or SWE (SWE-RS) could further improve the diagnostic performance remains to be investigated. We aimed to develop the B-US-RS and SWE-RS and determine their performances in classifying breast masses.

Materials and methods: This retrospective study included 291 women (mean age ± standard deviation, 40.9 ± 12.3 years) from two centers who had US-visible solid breast masses and underwent biopsy and/or surgical resection between June 2015 and July 2017. B-mode US and SWE images of the 198 masses in 198 patients (training cohort) from center 1 were segmented, respectively, to construct B-US-RS and SWE-RS using the least absolute shrinkage and selection operator regression and tested in an independent validation cohort of 65 masses in 65 patients from center 1 and in an external validation cohort of 28 masses in 28 patients from center 2. The performances of B-US-RS and SWE-RS were assessed using receiver operating characteristic (ROC) analysis and compared with that of radiologist assessment [Breast Imaging Reporting and Data System (BI-RADS)] and quantitative SWE parameters [maximum elasticity (E _max), mean elasticity (E _mean), elasticity ratio (E _ratio), and elastic modulus standard deviation (E _SD)] by using the McNemar test.

Results: The single best-performing quantitative SWE parameter, E _max, had a higher specificity than BI-RADS assessment in the training and independent validation cohorts (P < 0.001 for both). The areas under the ROC curves (AUCs) of B-US-RS and SWE-RS both were 0.99 (95% CI = 0.99-1.00) in the training cohort, 1.00 (95% CI = 1.00-1.00) in the independent validation cohort, and 1.00 (95% CI = 1.00-1.00) in the external validation cohort. The specificities of B-US-RS and SWE-RS were higher than that of E _max in the training (P < 0.001 for both) and independent validation cohorts (P = 0.02 for both).

Conclusion: The B-US-RS and SWE-RS outperformed the quantitative SWE parameters and BI-RADS assessment for classifying breast masses. The integration of the deep learning-based radiomics approach would help improve the classification ability of B-mode US and SWE for breast masses.

Keywords: breast neoplasms; deep learning; radiomics; shear-wave elastography; ultrasonography.