Dataset Information

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

ABSTRACT: Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites. Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3). Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = -6.67, 95%CI [-15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [-3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89. Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

SUBMITTER: Huang R

PROVIDER: S-EPMC6412152 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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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.

Huang Rongzhong R Jiang Lihong L Xu Yu Y Gong Yuping Y Ran Haitao H Wang Zhigang Z Sun Yang Y

Frontiers in oncology 20190305

<b>Background:</b> We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites. <b>Methods:</b> A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic o ...[more]

PMID: 30891425

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Project description:BackgroundThe value of contrast-enhanced ultrasound (CEUS), shear wave elastography (SWE) and their combination in the diagnosis of benign and malignant breast lesions have not been systematically evaluated. This study aimed to evaluate the diagnostic value of CEUS combined with SWE in benign and malignant breast lesions.MethodsWe searched six electronic databases for literature to evaluate the value of CEUS combined with SWE in the diagnosis of benign and malignant breast lesions from inception to May 2023. Review Manager 5.4 (Cochrane), Meta-DiSc 1.4, and Stata 14.0 (StataCorp) were used for meta-analysis. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated to evaluate the diagnostic performance.ResultsUltimately, 17 studies were analyzed including 1,962 lesions in total. The overall quality of the included literature was acceptable, and no significant publication bias was found among the included studies. The pooled diagnostic performance measures for CEUS were as follows: SEN: 0.86 [95% confidence interval (CI): 0.84-0.88], SPE: 0.78 (95% CI: 0.75-0.80), PLR: 4.10 (95% CI: 2.86-5.90), NLR: 0.20 (95% CI: 0.15-0.25), DOR: 23.68 (95% CI: 16.77-33.44), and AUC: 0.90 (95% CI: 0.87-0.93); while, for SWE, SEN: 0.83 (95% CI: 0.81-0.86), SPE: 0.81 (95% CI: 0.78-0.83), PLR: 4.36 (95% CI: 3.18-5.97), NLR: 0.22 (95% CI: 0.17-0.29), DOR: 23.13 (95% CI: 14.70-36.40), and AUC: 0.90 (95% CI: 0.87-0.92). The measures for the pooled diagnostic performance of CEUS combined with SWE were as follows: SEN: 0.92 (95% CI: 0.90-0.94), SPE: 0.87 (95% CI: 0.85-0.89), PLR: 7.10 (95% CI: 5.24-9.61), NLR: 0.11 (95% CI: 0.07-0.16), DOR: 83.51 (95% CI: 49.67-140.39), and AUC: 0.96 (95% CI: 0.94-0.98). There was no statistically significant difference in SEN, SPE, and accuracy (ACC) between CEUS and SWE (P>0.05), but they were significantly lower than those of CEUS combined with SWE (P<0.001).ConclusionsThe diagnostic performance of CEUS combined with SWE is higher than that of using CEUS or SWE alone and can further improve the diagnosis of breast lesions.

Project description:Background Elastography is a promising way to evaluate tissue differences regarding stiffness, and the stiffness of the malignant breast lesions increased at the lesion margin. However, there is a lack of data on the value of the shear wave elastography (SWE) parameters of the surrounding tissue (shell) of different diameter on the diagnosis of benign and malignant breast lesions. Therefore, the purpose of our study was to evaluate the diagnostic performance of shell elasticity in the diagnosis of benign and malignant breast lesions using SWE. Methods Between September 2016 and June 2017, women with breast lesions underwent both conventional ultrasound (US) and SWE. Elastic values of the lesions peripheral tissue were determined according to the shell size, which was automatically drawn along the edge of the lesion using the following software guidelines: (1): 1?mm; (2): 2?mm; and (3): 3?mm. Quantitative elastographic features of the inner lesions and shell, including the elasticity mean (Emean), elasticity maximum (Emax), and elasticity minimum (Emin), were calculated using an online-available software. The receiver operating characteristic curves (ROCs) of the elastographic features was analyzed to assess the diagnostic performance, and the area under curve (AUC) of each elastographic feature was obtained. Logistic regression analysis was used to predict significant factors of malignancy, permitting the design of predictive models. Results This prospective study included 63 breast lesions of 63 women. Of the 63 lesions, 33 were malignant and 30 were benign. The diagnostic performance of Emax-3shell was the highest (AUC?=?0.76) with a sensitivity of 60.6% and a specificity of 83.3%. According to stepwise logistic regression analysis, the Emax-3shell and the Emin-3shell were significant predictors of malignancy (p?<?0.05). The AUC of the predictive equation was 0.86. Conclusions SWE features, particularly the combination of Emax-3shell and Emin-3shell can improve the diagnosis of breast lesions.

Project description:ObjectivesThe aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.MethodsThis study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.ResultsThe multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.ConclusionsIn this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.Key points• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

Dataset Information

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

Publications

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

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