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Background and Aims: Endoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images. Methods: PubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy. Results: Overall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89–0.95), 0.82 (95% CI 0.75–0.87), 4.55 (95% CI 2.64–7.84), and 0.12 (95% CI 0.07–0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83–175.69) and 0.950 (Q* = 0.891). Conclusions: AI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future. [ABSTRACT FROM AUTHOR]

Abstract Background and aims Endoscopic ultrasound (EUS) elastography is a novel non-invasive technique that can be used for distinguishing benign from malignant pancreatic masses. However, the studies have reported widely varied sensitivities and specificities. A meta-analysis was performed to assess the performance of EUS elastography for the differentiation of benign and malignant pancreatic masses. Methods All the eligible studies were searched by PubMed, Medline, Embase, and the Cochrane Library. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and area under the curve (AUC) were calculated to examine the accuracy. Results A total of nineteen studies which included 1687 patients were analyzed. The pooled sensitivity and specificity for the diagnosis of malignant pancreatic masses were 0.98 (95% confidence interval [CI] 0.96–0.99) and 0.63 (95% CI 0.58–0.69) for qualitative EUS elastography, 0.95 (95% CI 0.93–0.97) and 0.61 (95% CI 0.56–0.66) for quantitative EUS elastography, respectively. The positive and negative LR were 2.60 (95% CI 1.84–3.66) and 0.05 (95% CI 0.02–0.10) for qualitative EUS elastography, 2.64 (95% CI 1.82–3.82) and 0.10 (95% CI 0.06–0.16) for quantitative EUS elastography, respectively. The summary diagnostic odds ratio (DOR) and the AUC were 60.59 (95% CI 28.12–130.56) and 0.91 (Q* = 0.842) for qualitative EUS elastography, 30.09 (95% CI 15.40–58.76) and 0.93 (Q* = 0.860) for quantitative EUS elastography. Conclusions Our meta-analysis shows that both qualitative and quantitative EUS elastography have high accuracy in the detection of malignant pancreatic masses, which could be used as a valuable complementary method to EUS-FNA for the differentiation of pancreatic masses in the future. [ABSTRACT FROM AUTHOR]

Purpose: The use of contrast-enhanced sonography (CEUS) has yielded promising results in the differentiation of thyroid nodules. We conducted this meta-analysis to assess its performance in identifying and distinguishing between benign and malignant thyroid nodules.Methods: PubMed, Medline, Embase, and the Cochrane Library were searched for studies published through the end of December 2013. Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve were calculated.Results: A total of 13 studies were included in this meta-analysis. For the diagnosis of malignant thyroid nodules worldwide, the overall mean rates of sensitivity and specificity of CEUS were 90% (95% confidence interval [CI], 88-93%) and 86% (95% CI, 83-89%), respectively. The summary diagnostic odds ratio was 52.83 (95% CI, 21.71-128.55), and the area under the curve for the summary receiver operating characteristic curve was 0.94 (95% CI, 0.90-0.98).Conclusions: This meta-analysis indicates that CEUS may be a valuable supplemental method, with high rates of sensitivity and specificity, to use for identifying and distinguishing between benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Abstract: The goal of the study described here was to assess the performance of real-time elastography (RTE) in the detection of prostate cancers using a meta-analysis. A literature search of PubMed, Medline, Embase and the Cochrane Library was conducted. Published studies that evaluated the diagnostic performance of RTE in the diagnosis of prostate cancer and using the histopathology of the radical prostatectomy specimen as a reference standard were included. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve were calculated to examine the accuracy of RTE. A total of seven studies that included 508 patients were analyzed. The pooled sensitivity and specificity for the diagnosis of prostate cancer by RTE were 0.72 (95% confidence interval: 0.70–0.74) and 0.76 (0.74–0.78), respectively. The summary diagnostic odds ratio was 12.59 (7.26–21.84), and the area under the curve was 0.841 (Q* = 0.773). In conclusion, RTE imaging has high accuracy in the detection of prostate cancers using the histopathology of the radical prostatectomy specimen as the reference standard and may reduce the number of core biopsies in the future. [Copyright &y& Elsevier]