scholarly journals Combination of shear wave elastography and BI-RADS in identification of solid breast masses

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xue Zheng ◽  
Fei Li ◽  
Zhi-Dong Xuan ◽  
Yu Wang ◽  
Lei Zhang
Keyword(s):  
Shear Wave ◽  
Breast Imaging ◽  
Color Doppler ◽  
Shear Wave Elastography ◽  
Definitive Diagnosis ◽  
Pathological Examination ◽  
Diagnostic Specificity ◽  
Breast Masses ◽  
Malignant Breast ◽  
Grade Ii

Abstract Background To explore the value of quantitative shear wave elastography (SWE) plus the Breast Imaging Reporting and Data System (BI-RADS) in the identification of solid breast masses. Methods A total of 108 patients with 120 solid breast masses admitted to our hospital from January 2019 to January 2020 were enrolled in this study. The pathological examination served as the gold standard for definitive diagnosis. Both SWE and BI-RADS grading were performed. Results Out of the 120 solid breast masses in 108 patients, 75 benign and 45 malignant masses were pathologically confirmed. The size, shape, margin, internal echo, microcalcification, lateral acoustic shadow, and posterior acoustic enhancement of benign and malignant masses were significantly different (all P < 0.05). The E mean, E max, SD, and E ratio of benign and malignant masses were significantly different (all P < 0.05). The E min was similar between benign and malignant masses (P > 0.05). The percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (P < 0.05). BI-RADS plus SWE yielded higher diagnostic specificity and positive predictive value than either BI-RADS or SWE; BI-RADS plus SWE yielded the highest diagnostic accuracy among the three methods (all P < 0.05). Conclusion SWE plus routine ultrasonography BI-RADS has a higher value in differentiating benign from malignant breast masses than color doppler or SWE alone, which should be further promoted in clinical practice.

scholarly journals Application of 3D and 2D quantitative shear wave elastography (SWE) to differentiate between benign and malignant breast masses

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jie Tian ◽  
Qianqi Liu ◽  
Xi Wang ◽  
Ping Xing ◽  
Zhuowen Yang ◽  
...  
Keyword(s):  
Shear Wave ◽  
Shear Wave Elastography ◽  
Breast Masses ◽  
Malignant Breast

scholarly journals Diagnostic role of shear wave elastography for differentiating benign and malignant breast masses

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Nichanametla Sravani ◽  
Ananthakrishnan Ramesh ◽  
Sathasivam Sureshkumar ◽  
Chellappa Vijayakumar ◽  
K.M. Abdulbasith ◽  
...  
Keyword(s):  
Shear Wave ◽  
Shear Wave Elastography ◽  
Breast Masses ◽  
Diagnostic Role ◽  
Malignant Breast

A qualitative and quantitative assessment of simultaneous strain, shear wave, and point shear wave elastography to distinguish malignant and benign breast lesions

2020 ◽  
pp. 028418512096142
Author(s):  
Yasemin Altıntas ◽  
Mehmet Bayrak ◽  
Ömer Alabaz ◽  
Medih Celiktas
Keyword(s):  
Shear Wave ◽  
Diagnostic Performance ◽  
Area Under The Curve ◽  
Shear Wave Elastography ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Breast Masses ◽  
Malignant Breast ◽  
The Mean ◽  
Us Elastography

Background Ultrasound (US) elastography has become a routine instrument in ultrasonographic diagnosis that measures the consistency and stiffness of tissues. Purpose To distinguish benign and malignant breast masses using a single US system by comparing the diagnostic parameters of three kinds of breast elastography simultaneously added to B-mode ultrasonography. Material and Methods A total of 163 breast lesions in 159 consecutive women who underwent US-guided core needle biopsy were included in this prospective study. Before the biopsy, the lesions were examined with B-mode ultrasonography and strain (SE), shear wave (SWE), and point shear wave (STQ) elastography. The strain ratio was computed and the Tsukuba score determined. The mean elasticity values using SWE and STQ were computed and converted to Young’s modulus E (kPa). Results All SE, SWE, and STQ parameters showed similar diagnostic performance. The SE score, SE ratio, SWEmean, SWEmax, STQmean, and STQmax yielded higher specificity than B-mode US alone to differentiate benign and malignant masses. The sensitivity of B-mode US, SWE, and STQ was slightly higher than that of the SE score and SE ratio. The SE score, SE ratio, SWEmean, SWEmax, STQmean, and STQmax had significantly higher positive predictive value and diagnostic accuracy than B-mode US alone. The area under the curve for each of these elastography methods in differentiating benign and malignant breast lesions was 0.93, 0.93, 0.98, 0.97, 0.98, and 0.96, respectively; P<0.001 for all measurements. Conclusion SE (ratio and score), SWE, and STQ had higher diagnostic performance individually than B-mode US alone in distinguishing between malignant and benign breast masses.

Differentiating solid breast masses: comparison of the diagnostic efficacy of shear wave elastography and magnetic resonance imaging

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Maryam Farghadani ◽  
Rozbeh Barikbin ◽  
Mostafa Haji Rezaei ◽  
Ali Hekmatnia ◽  
Marzieh Aalinezhad ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Shear Wave ◽  
Breast Imaging ◽  
Shear Wave Elastography ◽  
Diagnostic Value ◽  
Resonance Imaging ◽  
Diagnostic Agreement ◽  
Gold Standard Method ◽  
Breast Masses

AbstractObjectivesShear wave elastography (SWE) quantitatively determines the nature of the breast lesions. Few previous studies have compared the diagnostic value of this modality with other imaging techniques. The present study aimed to compare the diagnostic value of SWE with that of magnetic resonance imaging (MRI) in detecting the nature of the breast masses.MethodsIn this cross-sectional study, 80 patients with breast lumps who had Breast Imaging Reporting and Data System (BI-RADS) score of three or higher based on mammography and/or screening ultrasonography, underwent 3D SWE and MRI. The lesions were classified according to MRI BI-RADS scoring; Mean elasticity (Emean) and elasticity ratio (Eratio) for each lesion were also determined by SWE. The results of these two modalities were compared with histopathologic diagnosis as the gold standard method; diagnostic value and diagnostic agreement were then calculated.ResultsOf the masses, 46.2% were histopathologically proven to be malignant. The Emean for benign and malignant masses was 34.04 ± 19.51 kPa and 161.92 ± 58.14 kPa, respectively. Both modalities had diagnostic agreement with histopathologic results (p<0.001). Kappa coefficient was 0.87 for SWE and 0.42 for MRI. The sensitivity of both methods was 94.59% (95% CI: 81.81–99.34), while the specificity and accuracy were 48.84% [95% CI: 33.31–64.54] and 70.0% [95% CI: 58.72–79.74] for MRI, and 93.02% [95% CI: 80.94–98.54] and 93.75% [95% CI: 86.01–97.94] for SWE.ConclusionsSWE has better diagnostic value in terms of determining the nature of the breast masses. SWE can increase the diagnostic function of differentiating benign masses from malignant ones.

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

2019 ◽  
Vol 41 (04) ◽  
pp. 390-396 ◽  
Author(s):  
Ji Hyun Youk ◽  
Jin Young Kwak ◽  
Eunjung Lee ◽  
Eun Ju Son ◽  
Jeong-Ah Kim
Keyword(s):  
Feature Selection ◽  
Shear Wave ◽  
Diagnostic Performance ◽  
Univariate Analysis ◽  
Shear Wave Elastography ◽  
Grayscale Image ◽  
Lasso Regression ◽  
Breast Masses ◽  
Malignant Breast ◽  
Breast Radiologist

Abstract 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.

Classification of Breast Masses on Ultrasound Shear Wave Elastography using Convolutional Neural Networks

2020 ◽  
Vol 42 (4-5) ◽  
pp. 213-220 ◽  
Author(s):  
Tomoyuki Fujioka ◽  
Leona Katsuta ◽  
Kazunori Kubota ◽  
Mio Mori ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Shear Wave ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Diagnostic Performance ◽  
Shear Wave Elastography ◽  
Training Data ◽  
Breast Masses ◽  
Malignant Breast

We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844–0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance compared with radiologist readings. DenseNet169 with 100 epochs, Xception with 50 epochs, and Xception with 100 epochs had a better diagnostic performance compared with SWEc ( P = 0.018–0.037). Deep learning with CNNs exhibited equal or higher AUC compared with radiologists when discriminating benign from malignant breast masses on ultrasound SWE.

US-Elastography for Breast Lesion Characterization: Prospective Comparison of US BIRADS, Strain Elastography and Shear wave Elastography

2020 ◽  
Author(s):  
Vito Cantisani ◽  
Emanuele David ◽  
Richard G. Barr ◽  
Maija Radzina ◽  
Valeria de Soccio ◽  
...  
Keyword(s):  
Shear Wave ◽  
Diagnostic Performance ◽  
Color Doppler ◽  
Shear Wave Elastography ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Strain Elastography ◽  
Prospective Comparison ◽  
Malignant Breast ◽  
Sensitivity Specificity

Abstract Purpose To evaluate the diagnostic performance of strain elastography (SE) and 2 D shear wave elastography (SWE) and SE/SWE combination in comparison with conventional multiparametric ultrasound (US) with respect to improving BI-RADS classification results and differentiating benign and malignant breast lesions using a qualitative and quantitative assessment. Materials and Methods In this prospective study, 130 histologically proven breast masses were evaluated with baseline US, color Doppler ultrasound (CDUS), SE and SWE (Toshiba Aplio 500 with a 7–15 MHz wide-band linear transducer). Each lesion was classified according to the BIRADS lexicon by evaluating the size, the B-mode and color Doppler features, the SE qualitative (point color scale) and SE semi-quantitative (strain ratio) methods, and quantitative SWE. Histological results were compared with BIRADS, strain ratio (SR) and shear wave elastography (SWE) all performed by one investigator blinded to the clinical examination and mammographic results at the time of the US examination. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance of B-mode US, SE, SWE, and their combination. Results Histological examination revealed 47 benign and 83 malignant breast lesions. The accuracy of SR was statistically significantly higher than SWE (sensitivity, specificity and AUC were 89.2 %, 76.6 % and 0.83 for SR and 72.3 %, 66.0 % and 0.69 for SWE, respectively, p = 0.003) but not higher than B-mode US (B-mode US sensitivity, specificity and AUC were 85.5 %, 78.8 %, 0.821, respectively, p = 1.000). Conclusion Our experience suggests that conventional US in combination with both SE and SWE is a valid tool that can be useful in the clinical setting, can improve BIRADS category assessment and may help in the differentiation of benign from malignant breast lesions, with SE having higher accuracy than SWE.

Two-View versus Single-View Shear-Wave Elastography: Comparison of Observer Performance in Differentiating Benign from Malignant Breast Masses

Radiology ◽  
2014 ◽  
Vol 270 (2) ◽  
pp. 344-353 ◽  
Author(s):  
Su Hyun Lee ◽  
Nariya Cho ◽  
Jung Min Chang ◽  
Hye Ryoung Koo ◽  
Jin You Kim ◽  
...  
Keyword(s):  
Shear Wave ◽  
Shear Wave Elastography ◽  
Observer Performance ◽  
Breast Masses ◽  
Single View ◽  
Malignant Breast
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