scholarly journals Meta-analysis of contrast-enhanced ultrasound and contrast-enhanced harmonic endoscopic ultrasound for the diagnosis of gallbladder malignancy

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xue Liang ◽  
Xiang Jing
Keyword(s):  
Sensitivity And Specificity ◽  
Endoscopic Ultrasound ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Accurate Method ◽  
Contrast Enhanced Ultrasound ◽  
Summary Receiver Operating Characteristic ◽  
Contrast Enhanced ◽  
Heterogeneous Enhancement ◽  
Gallbladder Malignancy

Abstract Background The diagnosis between benign and malignant gallbladder lesions is sometimes difficult. The objective of this study is to assess whether contrast-enhanced ultrasound (CEUS) and contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) can be an accurate method for detecting gallbladder malignancy and to determine which imaging signs can be indicative of malignancy. Methods A study search of PubMed, Elsevier, and Sciencedirect was performed in May 2019. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve were used to examine the accuracy of CEUS and CH-EUS. Results Twenty-one studies were included in the meta-analysis. The pooled sensitivities of CEUS and CH-EUS were 0.81 (0.75–0.86) and 0.92 (0.86–0.95); the specificities were 0.94 (0.90–0.96) and 0.89 (0.69–0. 97); the DORs were 64 (32–127) and 89 (22–354); and the area under the SROC curves were 0.90 (0.87–0.92) and 0.92 (0.90–0.94). On CEUS, the diagnostic criterion for gallbladder malignancy according to four features were analyzed. Sensitivity and specificity were 0.75 (0.65–0.83) and 0.98 (0.85–1.00) for integrity of gallbladder wall; 0.69 (0.55–0.81) and 0.89 (0.77–0.95) for heterogeneous enhancement; 0.81 (0.71–0.88) and 0.88 (0.76–0.94) for irregular vessels; and 0.81 (0.66–0.91) and 0.75 (0.59–0.86) for washout time within 28 s. On CH-EUS, heterogeneous enhancement could be indicative of malignant lesions with a sensitivity of 0.94 (0.85–0.97); and the specificity was 0.92 (0.71–0.98). Conclusions CEUS and CH-EUS are promising and reliable imaging modalities with a high sensitivity and specificity for the diagnosis of gallbladder malignancy. CH-EUS might be more sensitive than CEUS with a higher sensitivity. In addition, irregular tralesional vessels and washout time within 28 s on CEUS and heterogeneous enhancement on CH-EUS are indicative of malignancy. However, larger scale and well-designed studies are warranted to verify our results.

scholarly journals Endoscopic Ultrasound vs. Computed Tomography for Gastric Cancer Staging: A Network Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Bogdan Silviu Ungureanu ◽  
Victor Mihai Sacerdotianu ◽  
Adina Turcu-Stiolica ◽  
Irina Mihaela Cazacu ◽  
Adrian Saftoiu
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
Endoscopic Ultrasound ◽  
Multidetector Ct ◽  
Meta Analysis ◽  
Clinical Stage ◽  
Preoperative Staging ◽  
Summary Receiver Operating Characteristic ◽  
Significant Difference

Gastric cancer preoperative staging is of outmost importance to assure proper management of the disease. Providing a relevant clinical stage relies on different imaging methods such as computed tomography (CT) or endoscopic ultrasound (EUS). We aimed to perform a network meta-analysis for gastric cancer clinical stage diagnostic tests, thus comparing the diagnostic accuracy of EUS vs. multidetector CT (MDCT) and EUS vs. EUS + MDCT. We plotted study estimates of pooled sensitivity and specificity on forest plots and summary receiver operating characteristic space to explore between-study variation in the performance of EUS, MDCT and EUS + MDCT for T1–T4, N0–N3, M0–M1 when data were available. Exploratory analyses were undertaken in RevMan 5. We included twelve studies with 2047 patients. Our results suggest that EUS was superior to MDCT in preoperative T1 and N staging. MDCT is more specific for the M stage but no significant difference in sensitivity was obtained. When comparing EUS vs. EUS + MDCT for T1 both sensitivity and specificity were not relevant. No significant differences were observed in T2–T4 stages. Even though EUS helped differentiate between the presence of invaded nodules, N stages should be carefully assessed by both methods since there is not sufficient data.

scholarly journals Accuracy of Various Forms of Contrast-Enhanced MRI for Diagnosing Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Chun Zhao ◽  
Hongyan Dai ◽  
Juwei Shao ◽  
Qian He ◽  
Wei Su ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Cochrane Library ◽  
Significant Heterogeneity ◽  
Chi Square ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri

BackgroundContrast-enhanced MRI can be used to identify patients with hepatocellular carcinoma (HCC). However, studies around the world have found differing diagnostic accuracies for the technique. Hence, we designed this meta-analysis to assess the accuracy of contrast-enhanced MRI for HCC diagnosis.MethodsWe conducted a systematic search for all studies reporting the diagnostic accuracy of contrast-enhanced MRI for HCC in the databases of MEDLINE, EMBASE, Cochrane Library, Web of Science, SCOPUS, ScienceDirect, and Google Scholar from inception until January 2021. We used the “Midas” package from the STATA software to perform the meta-analysis.ResultsOur study was based on 21 publications with 5,361 patients. The pooled HCC diagnosis sensitivity and specificity were 75% (95% CI, 70%–80%) and 90% (95% CI, 88%–92%), respectively, for gadoxetic acid-enhanced MRI; and they were 70% (95% CI, 57%–81%) and 94% (95% CI, 85%–97%), respectively, for MRI with extracellular contrast agents (ECA-MRI). We found significant heterogeneity with a significant chi-square test and an I2 statistic >75%. We also found significant publication bias as per Deeks’ test results and funnel plot.ConclusionWe found that both types of contrast-enhanced MRI are accurate diagnostic and surveillance tools for HCC and offer high sensitivity and specificity. Further studies on different ethnic populations are required to strengthen our findings.

Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer

2019 ◽  
Vol 85 (6) ◽  
pp. 645-653 ◽  
Author(s):  
Wei Jun ◽  
Wang Cong ◽  
Xie Xianxin ◽  
Jiang Daqing
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Cochrane Library ◽  
Operating Characteristics ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

The purpose of this meta-analysis was to determine the value of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) in evaluating the response of breast cancer to neoadjuvant chemotherapy (NAC). PubMed, Embase, and Cochrane Library databases (from building to July 31, 2018) were searched to collect articles about the therapeutic evaluation of NAC using the quantitative DCE-MRI in patients with breast cancer. The sensitivities and specificities of quantitative DCE-MRI in the evaluation of NAC for breast cancer were extracted from the articles. Meta-DiSc1.4 was applied to evaluate the efficacy of the sensitivity and specificity; forest figure and summary receiver operating characteristics (SROC) were created. A total of 356 articles were enrolled in this study, including 739 cases in total, in which 218 cases were effective and the other 521 cases were ineffective to NAC, considering the pathological results as the gold standard. The sensitivity and specificity in the included 14 articles of quantitative DCE-MRI ( Ktrans, kep, and ve) in comprehensively evaluating NAC for breast cancer were 84 per cent (95% confidence interval (CI): 78–88%) and 83 per cent (95% CI: 79–86%), respectively. The area under SROC was 0.899 (95% CI: 0.867–0.943). The sensitivity and specificity in the three articles of Ktrans evaluating NAC for breast cancer were 84.1 per cent (95% CI: 71.0–92.1%) and 81.3 per cent (95% CI: 70.5%-88.5%), respectively. The area under SROC was 0.899 (95% CI: 0.834–0.962). Our study confirmed that the quantitative DCE-MRI is able to monitor NAC treatment for breast cancer because of its high sensitivity and specificity. However, there is a high degree of heterogeneity in published studies, highlighting the lack of standardization in the field.

Diagnostic Accuracy of Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) for Differentiating Between Hepatocellular Carcinoma and Other Hepatic Malignancies in High-Risk Patients: A Meta-Analysis

2020 ◽  
Author(s):  
Jiawu Li ◽  
Lulu Yang ◽  
Lin Ma ◽  
Qiang Lu ◽  
Yan Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Liver Imaging ◽  
Contrast Enhanced Ultrasound ◽  
High Risk Patients ◽  
Contrast Enhanced ◽  
Risk Patients ◽  
Hepatic Malignancies

Abstract Objectives The American College of Radiology (ACR) contrast-enhanced ultrasound liver imaging reporting and data system (CEUS LI-RADS), which includes diagnostic criteria for hepatocellular carcinoma (HCC) and other hepatic malignancies (OM), is increasingly used in clinical practice. This study performed a meta-analysis to assess the diagnostic accuracy of CEUS LI-RADS for differentiating between HCC and OM in high-risk patients. Methods PubMed, Embase (Ovid), and Cochrane (CENTRAL) were searched for relevant studies. All studies that reported the percentage of HCC and OM in the LI-RADS categories were included. Random-effects models were used to calculate the pooled sensitivity and specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve. Results Eight studies involving 4215 focal liver lesions were included in the final analysis. The pooled sensitivity and specificity of the LR-5 criteria for HCC were 0.71 (95 % CI, 0.69–0.72) and 0.88 (0.85–0.91), respectively, the DOR was 18.36 (7.41–45.52), and the area under the SROC curve (AUC) was 0.8128. The pooled sensitivity and specificity of the LR-M criteria for OMs were 0.85 (0.81–0.88) and 0.86 (0.85–0.87), the DOR was 27.82 (11.83–65.40), respectively, and the SROC AUC was 0.9098. Conclusion The CEUS LI-RADS can effectively distinguish HCC from other hepatic malignancy in high-risk patients based on LR-5 criteria and LR-M criteria. However, further studies are needed for validation due to the limited number of included studies and the potential heterogeneity among the included studies.

scholarly journals The Value of Contrast-Enhanced Ultrasound (CEUS) in Differentiating Testicular Masses: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (19) ◽  
pp. 8990
Author(s):  
Antonio Tufano ◽  
Rocco Simone Flammia ◽  
Luca Antonelli ◽  
Rocco Minelli ◽  
Giorgio Franco ◽  
...  
Keyword(s):  
Systematic Review ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Contrast Enhanced Ultrasound ◽  
Summary Receiver Operating Characteristic ◽  
Diagnostic Modality ◽  
Contrast Enhanced ◽  
Meta Analyses ◽  
Sensitivity Specificity

Ultrasound (US) still represents the mainstay of scrotal imaging. However, contrast-enhanced ultrasound (CEUS) is a relatively novel, but increasingly utilized diagnostic modality. In consequence, we performed a systematic review (SR) and pooled meta-analysis to investigate the diagnostic performance of CEUS in the evaluation of testicular masses (TM). A SR up to June 2021 was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The diagnostic performance of CEUS was evaluated basing on two different endpoints: neoplastic vs. non-neoplastic and malignant vs. benign TM. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) from eligible studies were pooled and summary receiver operating characteristic (SROC) curves were constructed for each endpoint. Overall, six qualified studies were deemed suitable for this meta-analysis. Diagnostic performance of CEUS showed an accuracy of 0.96 in detecting neoplastic masses (sensitivity of 0.89, PPV of 0.85, specificity of 0.62, and NPV of 0.69) and an accuracy of 0.96 in detecting malignant masses (sensitivity of 0.86, PPV of 0.73, specificity of 0.87, and NPV of 0.91). Taken together, CEUS may represent a promising minimally invasive diagnostic tool for characterization of TM, since it allows clinicians to identify neoplastic lesions and exclude malignant tumor.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

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