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

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.

Machine learning–based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study

“…Quantitative SWE parameters used alone have been able to classify breast lesions with a specificity of 86% and a sensitivity of 84% (27). Our results also agree with a previous study that found Emax to be the best-performing parameter in classifying breast lesions, achieving the highest AUC of 0.90 (95% CI, 0.77-1.00) (15). Similarly, the integration of SWE and B-US has been shown to improve diagnostic efficacy in breast cancer screening, particularly in specificity (28).…”

“…Furthermore, there were 77 patients enrolled in the external validation cohort 1 and 55 in the external validation cohort 2, making a total of 259 patients in our study. Compared to previously published studies, Zhang et al conducted a retrospective study of 291 women from 2 centers to compare the diagnostic performance between B-US and SWE in classifying breast masses (15). Patients were divided into a training cohort (n=198), an independent validation cohort (n=65), and an external test cohort (n=28).…”

“…Data were retrospectively collected from April 2018 to May 2020. The sample size calculation was based on previously published studies, with an expected sensitivity of 0.8 and an expected specificity of 0.9 used in the final calculation (15)(16)(17). Alpha (α) was 0.05, with an allowable error of 10%.…”

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

“…In their study, seven such features achieved an AUC of 0.917 for the identification of breast malignancies. A more recent study by Zhang X. et al compared deep-learning based radiomics scores acquired from B-mode US and shear-wave elastography (SWE) with the results of the BI-RADS assessment and quantitative SWE parameters and found a significant increase in the diagnostic performance by using radiomics scores, reaching an AUC of 1 for both [75]. On the preclinical level, Theek et al performed a radiomics analysis of CEUS data from different mouse tumor models, achieving correct classifications in 82.1 % of cases [76].…”

A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer

“…The performance of the brain metastases prediction model (under 10-fold cross-validation mode) The confusion matrix of the Naïve Bayes brain metastases prediction model (the percentages were calculated based on actual value) The triple-negative breast cancer brain metastases risk-prediction nomogram (based on whole cohort data and Naïve Bayes method). treatment response prediction(7,(21)(22)(23)(24)(25). Braman et al used peritumoral radiomics characteristics to predict the tumor microenvironmental status and treatment response of HER-2 positive breast cancer(26).…”