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

2017 ◽  
Vol 59 (7) ◽  
pp. 789-797 ◽  
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.

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.

scholarly journals Qualitative and quantitative strain and shear wave elastography paradigm in differentiation of breast lesions

2022 ◽  
Vol 53 (1) ◽  
Author(s):  
Roaa M. A. Shehata ◽  
Mostafa A. M. El-Sharkawy ◽  
Omar M. Mahmoud ◽  
Hosam M. Kamel
Keyword(s):  
Shear Wave ◽  
Diagnostic Performance ◽  
Strain Ratio ◽  
Breast Lesions ◽  
Breast Masses ◽  
Significant Difference ◽  
Malignant Breast ◽  
Malignant Lesions ◽  
The Mean ◽  
Sensitivity Specificity

Abstract Background Breast cancer is the most common life-threatening cancer in women worldwide. A high number of women are going through biopsy procedures for characterization of breast masses every day and yet 75% of the pathological results prove these masses to be benign. Ultrasound (US) elastography is a non-invasive technique that measures tissue stiffness. It is convenient for differentiating benign from malignant breast tumors. Our study aims to evaluate the role of qualitative ultrasound elastography scoring (ES), quantitative mass strain ratio (SR), and shear wave elasticity ratio (SWER) in differentiation between benign and malignant breast lesions. Results Among 51 female patients with 77 histopathologically proved breast lesions, 57 breast masses were malignant and 20 were benign. All patients were examined by B-mode ultrasound then strain and shear wave elastographic examinations using ultrasound machine (Logiq E9, GE Medical Systems) with 8.5–12 MHz high-frequency probes. Our study showed that ES best cut-off point > 3 with sensitivity, specificity, PPV, NPP, accuracy was 94.7%, 85%, 94.7%, 85%, 90.9%, respectively, and AUC = 0.926 at P < 0.001, mass SR the best cut-off point > 4.6 with sensitivity, specificity, PPV, NPP, accuracy was 96.5%, 80%, 93.2%, 88.9%, 92.2%, respectively, and AUC = 0.860 at P < 0.001, SWER the best cut-off value > 4.9 with sensitivity, specificity, PPV, NPP and accuracy was 91.2%, 80%, 92.9%, 76.2%, 93.5%, respectively, and AUC = 0.890 at P < 0.001. The mean mass strain ratio for malignant lesions is 10.1 ± 3.7 SD and for solid benign lesions 4.7 ± 4.3 SD (p value 0.001). The mean shear wave elasticity ratio for malignant lesions is 10.6 ± 5.4 SD and for benign (solid and cystic) lesions 3.6 ± 4.2 SD. Using ROC curve and Youden index, the difference in diagnostic performance between ES, SR and SWER was not significant in differentiation between benign and malignant breast lesions and also was non-significant difference when comparing them with conventional US alone. Conclusion ES, SR, and SWER have a high diagnostic performance in differentiating malignant from benign breast lesions with no statistically significant difference between them.

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.

Ruling-in and Ruling-out Significant Fibrosis and Cirrhosis in Patients with Chronic Hepatitis C Using a Shear Wave Measurement Method

2017 ◽  
Vol 26 (2) ◽  
pp. 139-143 ◽  
Author(s):  
Giovanna Ferraioli ◽  
Laura Maiocchi ◽  
Raffaella Lissandrin ◽  
Carmine Tinelli ◽  
Annalisa De Silvestri ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Receiver Operating Characteristic ◽  
Shear Wave ◽  
Operating Characteristic ◽  
Transient Elastography ◽  
Liver Stiffness ◽  
Shear Wave Elastography ◽  
Significant Fibrosis ◽  
Wave Measurement

Aims: To prospectively assess the cutoff values of a point shear wave measurement (SWM) method for ruling-in and ruling-out significant fibrosis and cirrhosis using transient elastography (TE) as the reference standard.Method: Consecutive patients with chronic hepatitis C were enrolled. Liver stiffness was assessed with the SWM method implemented on the HI VISION Ascendus ultrasound system (Hitachi Ltd, Japan) and with the TE method of the FibroScan® device (Echosens, France). For staging significant fibrosis (F≥2) and cirrhosis (F=4) we used the TE cutoffs of 7.0 and 12.0 kiloPascal (kPa), respectively. The diagnostic performance of SWM was assessed by calculating the area under the receiver operating characteristic (AUROC) curve. Cutoffs with specificity or sensitivity > 90% were chosen to rule-in or rule-out F≥2 and F=4.Results: 445 individuals [235 males, 210 females; mean age, 61.1 (13.3) years] were studied: 190 (42.7%) individuals had F0-F1 fibrosis stage, 82 (18.4%) F2, 46 (10.3%) F3, and 127 (28.6%) F4 fibrosis stage. For ruling-in F≥2 the SWM cutoff was 6.78 kPa [sensitivity, 76.9%(70.6-82.4); specificity, 90.3% (85.0-94.3)] and for ruling-out it was 5.55 kPa [sensitivity, 90.6% (85.8-94.1); specificity, 72.2% (64.9-78.6)]. For ruling-in F=4 the SWM cutoff was 9.15 kPa [sensitivity, 83.3% (74.4-90.2); specificity, 90.1% (86.0-93.2)] and for ruling-out it was 8.41 kPa [sensitivity, 90.6% (82.9-95.6); specificity, 82.2% (77.3-86.4)]. AUROCs were 0.92 (0.89-0.94) for F≥2 and 0.94 (0.91-0.96) for F=4.Conclusions. In clinical practice, the use of a dual cutoff of SWM may increase the confidence in staging liver fibrosis with a non-invasive shear wave elastography technique.Abbreviations: ARFI: acoustic radiation force impulse; AUROC: area under the ROC curve; CCC: concordance correlation coefficient; IQR/M: interquartile range/median; LSM: liver stiffness measurement; ROC: receiver operating characteristic; pSWE: point shear wave elastography; SWM: shear wave measurement; TE: transient elastography; US: ultrasound.

scholarly journals Impact of region of interest (ROI) size on the diagnostic performance of shear wave elastography in differentiating solid breast lesions

2017 ◽  
Vol 59 (6) ◽  
pp. 657-663 ◽  
Author(s):  
Jin Hee Moon ◽  
Ji-Young Hwang ◽  
Jeong Seon Park ◽  
Sung Hye Koh ◽  
Sun-Young Park
Keyword(s):  
Shear Wave ◽  
Sensitivity And Specificity ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Region Of Interest ◽  
Shear Wave Elastography ◽  
Breast Lesions ◽  
Malignant Breast ◽  
Solid Lesions ◽  
The Impact

Background Shear wave elastography (SWE) using a region of interest (ROI) can demonstrate the quantitative elasticity of breast lesions. Purpose To prospectively evaluate the impact of two different ROI sizes on the diagnostic performance of SWE for differentiating benign and malignant breast lesions. Material and Methods A total of 154 breast lesions were included. Two types of ROIs were investigated: one involving an approximately 2-mm diameter, small round ROIs placed over the stiffest area of the lesion, as determined by SWE (ROI-S); and another ROI drawn along the margin of the lesion using a touch pen or track ball to encompass the entire lesion (ROI-M). Maximum elasticity (Emax), mean elasticity (Emean), minimum elasticity (Emin), and standard deviation (SD) were measured for the two ROIs. The area under the receiver operating characteristic curve (AUC) as well as the sensitivity and specificity of each elasticity value were determined. Results The AUCs for ROI-S were higher than those for ROI-M when differentiating benign and malignant breast solid lesions. The Emax, Emean, Emin, and SD of the elasticity values for ROI-S were 0.865, 0.857, 0.816, and 0.849, respectively, and for ROI-M were 0.820, 0.780, 0.724, and 0.837, respectively. However, only Emax ( P = 0.0024) and Emean ( P = 0.0015) showed statistically significant differences. For ROI-S, the sensitivity and specificity of Emax were 78.8% and 84.3%, respectively, and those for Emean were 80.8% and 81.4%, respectively. Conclusion Using ROI-S with Emax and Emean has better diagnostic performance than ROI-M for differentiating between benign and malignant breast lesions.

scholarly journals Combination of Maximum Shear Wave Elasticity Modulus and TIRADS Improves the Diagnostic Specificity in Characterizing Thyroid Nodules: A Retrospective Study

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jing Hang ◽  
Fan Li ◽  
Xiao-hui Qiao ◽  
Xin-hua Ye ◽  
Ao Li ◽  
...  
Keyword(s):  
Shear Wave ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Thyroid Nodules ◽  
Elasticity Modulus ◽  
Shear Wave Elastography ◽  
Diagnostic Value ◽  
Diagnostic Specificity ◽  
Log Normal ◽  
Log Normal Distribution

Objectives. The present study is aimed at evaluating the diagnostic value of combining shear wave elastography (SWE) parameters and the thyroid imaging reporting and data system (TIRADS) for differentiating between benign and malignant thyroid nodules. Methods. Patients who underwent conventional ultrasonography (US) and SWE before surgery were enrolled in the current study. Each nodule was given a TIRADS risk score. The effectiveness of the SWE parameters was assessed by odds ratios (ORs). The SWE scoring risk stratification was proposed beyond 95% probability, and the desired values were obtained according to the log-normal distribution. The area under the receiver-operating characteristic (AUC) was used to compare the diagnostic performance between TIRADS-alone and TIRADS + SWE. Results. A total of 262 patients with 298 thyroid nodules were enrolled in our study. The pathological analyses were conducted on 121 benign and 177 malignant nodules. The AUC values for TIRADS-alone and TIRADS + SWE were 0.896 (accuracy 83.2%) and 0.917 (accuracy 84.2%), respectively. However, the TIRADS + SWE scores showed a higher specificity (88.4%) and positive predictive value (91.2%) as compared with the TIRADS-alone of 73.6% and 83.2%, respectively. Conclusions. Combining SWE and TIRADS improves the specificity of TIRADS-alone in differentiating between benign and malignant thyroid nodules.

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.

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