Diagnostic performance of qualitative shear-wave elastography according to different color map opacities for breast masses

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.

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Performance of shear-wave elastography for breast masses using different region-of-interest (ROI) settings

Acta Radiologica ◽

10.1177/0284185117735562 ◽

2017 ◽

Vol 59 (7) ◽

pp. 789-797 ◽

Cited By ~ 6

Author(s):

Ji Hyun Youk ◽

Eun Ju Son ◽

Kyunghwa Han ◽

Hye Mi Gweon ◽

Jeong-Ah Kim

Keyword(s):

Standard Deviation ◽

Receiver Operating Characteristic ◽

Shear Wave ◽

Roc Curve ◽

Diagnostic Performance ◽

Operating Characteristic ◽

Region Of Interest ◽

Shear Wave Elastography ◽

Breast Masses ◽

The Mean

Background Various size and shape of region of interest (ROI) can be applied for shear-wave elastography (SWE). Purpose To investigate the diagnostic performance of SWE according to ROI settings for breast masses. Material and Methods To measure elasticity for 142 lesions, ROIs were set as follows: circular ROIs 1 mm (ROI-1), 2 mm (ROI-2), and 3 mm (ROI-3) in diameter placed over the stiffest part of the mass; freehand ROIs drawn by tracing the border of mass (ROI-M) and the area of peritumoral increased stiffness (ROI-MR); and circular ROIs placed within the mass (ROI-C) and to encompass the area of peritumoral increased stiffness (ROI-CR). Mean (Emean), maximum (Emax), and standard deviation (ESD) of elasticity values and their areas under the receiver operating characteristic (ROC) curve (AUCs) for diagnostic performance were compared. Results Means of Emean and ESD significantly differed between ROI-1, ROI-2, and ROI-3 ( P < 0.0001), whereas means of Emax did not ( P = 0.50). For ESD, ROI-1 (0.874) showed a lower AUC than ROI-2 (0.964) and ROI-3 (0.975) ( P < 0.002). The mean ESD was significantly different between ROI-M and ROI-MR and between ROI-C and ROI-CR ( P < 0.0001). The AUCs of ESD in ROI-M and ROI-C were significantly lower than in ROI-MR ( P = 0.041 and 0.015) and ROI-CR ( P = 0.007 and 0.004). Conclusion Shear-wave elasticity values and their diagnostic performance vary based on ROI settings and elasticity indices. Emax is recommended for the ROIs over the stiffest part of mass and an ROI encompassing the peritumoral area of increased stiffness is recommended for elastic heterogeneity of mass.

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

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/a-0917-6825 ◽

2019 ◽

Vol 41 (04) ◽

pp. 390-396 ◽

Cited By ~ 1

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.

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Visually assessed colour overlay features in shear-wave elastography for breast masses: quantification and diagnostic performance

European Radiology ◽

10.1007/s00330-012-2647-3 ◽

2012 ◽

Vol 23 (3) ◽

pp. 658-663 ◽

Cited By ~ 47

Author(s):

Hye Mi Gweon ◽

Ji Hyun Youk ◽

Eun Ju Son ◽

Jeong-Ah Kim

Keyword(s):

Shear Wave ◽

Diagnostic Performance ◽

Shear Wave Elastography ◽

Breast Masses ◽

Colour Overlay

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

Ultrasonic Imaging ◽

10.1177/0161734620932609 ◽

2020 ◽

Vol 42 (4-5) ◽

pp. 213-220 ◽

Cited By ~ 4

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.

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