Diagnostic Value of Transthoracic Echocardiography Combined With Contrast-Enhanced Ultrasonography in Mediastinal Masses

2018 ◽  
Vol 38 (2) ◽  
pp. 415-422 ◽  
Author(s):  
Ding Wang ◽  
Jun Zhang ◽  
Yani Liu ◽  
Ruiying Sun ◽  
Jie Tian ◽  
...  
Keyword(s):  
Transthoracic Echocardiography ◽  
Diagnostic Value ◽  
Mediastinal Masses ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography

scholarly journals Comparison of conventional, doppler and contrast-enhanced ultrasonography in differential diagnosis of ovarian masses: a systematic review and meta-analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e052830
Author(s):  
Lizhang Xun ◽  
Lamei Zhai ◽  
Hui Xu
Keyword(s):  
Systematic Review ◽  
Ovarian Cancer ◽  
Data Extraction ◽  
Meta Analysis ◽  
Diagnostic Value ◽  
Doppler Us ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography ◽  
Sensitivity Specificity ◽  
Ovarian Masses

ObjectivesTo assess the value of conventional, Doppler and contrast-enhanced ultrasonography (CEUS) (conventional ultrasonography (US), Doppler US and CEUS) for diagnosing ovarian cancer.DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase and the Cochrane Library were conducted for studies published until October 2021.Eligibility criteriaStudies assessed the diagnostic value of conventional US, Doppler US or CEUS for detecting ovarian cancer, with no restrictions placed on published language and status.Data extraction and synthesisThe study selection and data extraction were performed by two independent authors. The sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic OR (DOR) and area under the receiver operating characteristic curve (AUC) were pooled using the bivariate generalised linear mixed model and random effects model.ResultsThe meta-analysis included 72 studies and involved 9296 women who presented with ovarian masses. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for conventional US were 0.91 (95% CI: 0.87 to 0.94) and 0.87 (95% CI: 0.82 to 0.91), 6.87 (95% CI: 4.98 to 9.49) and 0.10 (95% CI: 0.07 to 0.15), 57.52 (95% CI: 36.64 to 90.28) and 0.95 (95% CI: 0.93 to 0.97), respectively. The sensitivity, specificity, PLR, NLR, DOR and AUC for Doppler US were 0.93 (95% CI: 0.91 to 0.95) and 0.85 (95% CI: 0.80 to 0.89), 6.10 (95% CI: 4.59 to 8.11) and 0.08 (95% CI: 0.06 to 0.11), 61.76 (95% CI: 39.99 to 95.37) and 0.96 (95% CI: 0.94 to 0.97), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR and AUC for CEUS were 0.97 (95% CI: 0.92 to 0.99) and 0.92 (95% CI: 0.85 to 0.95), 11.47 (95% CI: 6.52 to 20.17) and 0.03 (95% CI: 0.01 to 0.09), 152.11 (95% CI: 77.77 to 297.51) and 0.99 (95% CI: 0.97 to 0.99), respectively. Moreover, the AUC values for conventional US (p=0.002) and Doppler US (p=0.005) were inferior to those of CEUS.ConclusionsConventional US, Doppler US and CEUS have a relatively high differential diagnostic value for differentiating between benign and malignant ovarian masses. The diagnostic performance of CEUS was superior to that of conventional US and Doppler US.

scholarly journals Diagnostic value of contrast-enhanced ultrasonography in the characterization of ovarian tumors

2010 ◽  
Vol 13 (1) ◽  
pp. 9-15 ◽  
Author(s):  
L.M. Sconfienza ◽  
N. Perrone ◽  
A. Delnevo ◽  
F. Lacelli ◽  
C. Murolo ◽  
...  
Keyword(s):  
Diagnostic Value ◽  
Ovarian Tumors ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography

scholarly journals Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ling Guo
Keyword(s):  
Unsupervised Learning ◽  
Predictive Value ◽  
Nipple Discharge ◽  
Diagnostic Value ◽  
Pathological Examination ◽  
Network Layers ◽  
Contrast Enhanced ◽  
Before And After ◽  
Conventional Ultrasound ◽  
Contrast Enhanced Ultrasonography

This paper aims to explore the application value of SonoVue contrast-enhanced ultrasonography based on deep unsupervised learning (DNS) in the diagnosis of nipple discharge. In this paper, a new model (ODNS) is proposed based on the unsupervised learning model and stack self-coding network. The ultrasonic images of 1,725 patients with breast lesions in the shared database are used as the test data of the model. The differences in accuracy (Acc), recall (RE), sensitivity (Sen), and running time between the two models before and after optimization and other algorithms are compared. A total of 48 female patients with nipple discharge are enrolled. The differences in SE, specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of conventional ultrasound and contrast-enhanced ultrasonography are analyzed based on pathological examination results. The results showed that when the number of network layers is 5, the classification accuracies of DNS and ODNS model data reached the highest values, which were 91.45% and 98.64%, respectively.

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