Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

Huang, Rongzhong; Jiang, Lihong; Xu, Yu Ping; Gong, Yuping; Ran, Haitao; Wang, Zhigang; Sun, Yong

doi:10.3389/fonc.2019.00102

Cited by 26 publications

(21 citation statements)

References 116 publications

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“…This approach has high sensitivities ranging from 95 to 97%, but low specificities ranging from 55 to 68%, in the differentiation between benign and malignant breast masses ( 33 ). Quantitative SWE parameters have been reported to be able to classify breast lesions with a specificity of 86% and a sensitivity of 84% ( 14 ). Among the quantitative SWE parameters, E max , E mean , E ratio , and E SD are the most commonly used indexes for the differential diagnosis ( 7 , 22 ).…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have shown that quantitative SWE parameters are reproducible for assessing elastographic features of breast masses and can improve the diagnostic specificity of B-mode US without loss of sensitivity ( 5 , 7 , 13 ). However, the specificity remains limited up to 86% when the quantitative SWE parameters were used ( 14 ). Therefore, a method to improve the diagnostic performance, especially to further improve the specificity of B-mode US or SWE, for the classification of breast lesions is needed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Since the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) released the Guidelines and recommendations for the hepatic use of contrast-enhanced ultrasound (CEUS), CEUS has been used for non-hepatic applications because of its advantages in visualizing the microcirculation and the dynamic enhancing process of tumors (5,9,10). Previous studies have shown the good performance of CEUS in differentiating benign and malignant thyroid nodules and screening appropriate nodules for FNA, as an effective supplement technique of conventional US (11)(12)(13). And the diagnostic accuracy of thyroid nodules can be increased by combining conventional US and CEUS (14,15).…”

Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced Ultrasound in the Differential Diagnosis and Risk Stratification of ACR TI-RADS Category 4 and 5 Thyroid Nodules With Non-Hypovascular

et al. 2021

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ObjectiveThis study aims to investigate the value of contrast-enhanced ultrasound (CEUS) in the differential diagnosis and risk stratification of ACR TI-RADS category 4 and 5 thyroid nodules with non-hypovascular.MethodsFrom January 2016 to December 2019 in our hospital, 217 ACR TI-RADS category 4 and 5 nodules with non-hypovascular in 210 consecutive patients were included for a derivation cohort. With surgery and/or fine-needle aspiration (FNA) as a reference, conventional ultrasound (US) features and CEUS features were analyzed. Multivariate logistic regression analysis was used to screen the independent risk factors and establish a risk predictive model. Between January 2020 and March 2021, a second cohort of 100 consecutive patients with 101 nodules were included for an external validation cohort. The model was converted into a simplified risk score and was validated in the validation cohort. The area under the receiver operating characteristic curves (AUC) were used to assess the models’ diagnostic performance.ResultsMicro-calcification, irregular margin, earlier wash-out, centripetal enhancement, and absence of ring enhancement were independent risk factors and strongly discriminated malignancy in the derivation cohort (AUC = 0.921, 95% CI 0.876–0.953) and the validation cohort (0.900, 0.824–0.951). There was no significant difference (P = 0.3282) between the conventional US and CEUS in differentiating malignant non-hypovascular thyroid nodules, but a combination of them (the predictive model) had better performance than the single method (all P <0.05), with a sensitivity of 87.0%, specificity of 86.2%, and accuracy of 86.6% in the derivation cohort. The risk score based on the independent risk factors divided non-hypovascular thyroid nodules into low-suspicious (0–3 points; malignancy risk <50%) and high-suspicious (4–7 points; malignancy risk ≥ 50%), the latter with nodule ≥10mm was recommended for FNA. The risk score showed a good ability of risk stratification in the validation cohort. Comparing ACR TI-RADS in screening suitable non-hypovascular nodules for FNA, the risk score could avoid 30.8% benign nodules for FNA.ConclusionsCEUS is helpful in combination with conventional US in differentiating ACR TI-RADS category 4 and 5 nodules with non-hypovascular. The risk score in this study has the potential to improve the diagnosis and risk stratification of non-hypovascular thyroid nodules.

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“…Since SWE was brought in clinical practice a number of studies have been conducted on its reliability and compared it with the conventional modes of imaging including ultrasound and mammograms. In addition to individual studies a number of meta-analysis has also been done [4][5][6]. Invariably all the studies showed superiority of SWE over ultrasound and mammograms alone in particular BIRAD 3 and 4 cases.…”

Section: Reliability Of Swe As Imaging Modality In Breast 51 Swe Difmentioning

confidence: 99%

Shearwave Elastography in Differentiating Benign and Malignant Breast Lesions

Syed¹,

Qureshi²,

Devrajani³

2020

Ultrasound Elastography

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Shearwave elastography is a new advance technique of the ultrasound with ultrafast shearwave mode which displays evaluation of the elasticity in real time. As the disease process tend to affect stiffness of the tissue thereby distorting its architecture. This architectural change makes the basic principal of the palpation part of the clinical examination. The shearwave elastography uses the principal of palpation. The output of shearwave is displayed in qualitative mode in the form of color change (ranging from blue to red) and quantitative mode as measure of elasticity in kilopascals (ranging from 0 to 300). The soft tissues are penetrated easily giving a homogenous pattern with blue to green color while cancers show color from red to dark red portraying high elasticity. The scoring system for interpretation of the shearwave results suggest that benign lesions show less (i.e., <200 kPa) elasticity while cancers reach high levels (upto 300 kPa). Shearwave elastography has shown superiority as compared to B-mode ultrasound and mammogram in determining the nature of the breast lesions. It has shown high sensitivity in BIRAD 3 and 4 lesions to downgrade and helps in making accurate diagnosis. It has also shown potential in predicting response of neoadjuvant chemotherapy.

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Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

Cited by 26 publications

References 116 publications

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

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

Contrast-Enhanced Ultrasound in the Differential Diagnosis and Risk Stratification of ACR TI-RADS Category 4 and 5 Thyroid Nodules With Non-Hypovascular

Shearwave Elastography in Differentiating Benign and Malignant Breast Lesions

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