scholarly journals Diagnostic accuracy of DNA methylation in detection of gastric cancer: a meta-analysis

Oncotarget ◽  
2017 ◽  
Vol 8 (68) ◽  
pp. 113142-113152 ◽  
Author(s):  
Weiling Hu ◽  
Wenfang Zheng ◽  
Qifang Liu ◽  
Hua Chu ◽  
Shujie Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Diagnostic Accuracy ◽  
Meta Analysis

scholarly journals Salivary DNA Methylation as an Epigenetic Biomarker for Head and Neck Cancer. Part I: A Diagnostic Accuracy Meta-Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 568
Author(s):  
Óscar Rapado-González ◽  
Cristina Martínez-Reglero ◽  
Ángel Salgado-Barreira ◽  
Laura Muinelo-Romay ◽  
Juan Muinelo-Lorenzo ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Head And Neck Cancer ◽  
Diagnostic Accuracy ◽  
Head And Neck ◽  
Likelihood Ratio ◽  
Neck Cancer ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Epigenetic Mechanism ◽  
Cochrane Library

DNA hypermethylation is an important epigenetic mechanism for gene expression inactivation in head and neck cancer (HNC). Saliva has emerged as a novel liquid biopsy representing a potential source of biomarkers. We performed a comprehensive meta-analysis to evaluate the overall diagnostic accuracy of salivary DNA methylation for detecting HNC. PubMed EMBASE, Web of Science, LILACS, and the Cochrane Library were searched. Study quality was assessed by the Quality Assessment for Studies of Diagnostic Accuracy-2, and sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (dOR), and their corresponding 95% confidence intervals (CIs) were calculated using a bivariate random-effect meta-analysis model. Meta-regression and subgroup analyses were performed to assess heterogeneity. Eighty-four study units from 18 articles with 8368 subjects were included. The pooled sensitivity and specificity of salivary DNA methylation were 0.39 and 0.87, respectively, while PLR and NLR were 3.68 and 0.63, respectively. The overall area under the curve (AUC) was 0.81 and the dOR was 8.34. The combination of methylated genes showed higher diagnostic accuracy (AUC, 0.92 and dOR, 36.97) than individual gene analysis (AUC, 0.77 and dOR, 6.02). These findings provide evidence regarding the potential clinical application of salivary DNA methylation for HNC diagnosis.

scholarly journals Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

2021 ◽  
Author(s):  
Swathikan Chidambaram ◽  
Viknesh Sounderajah ◽  
Nick Maynard ◽  
Sheraz R. Markar
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Systematic Review ◽  
Esophageal Cancer ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Meta Analysis ◽  
Diagnostic Methods ◽  
Gastrointestinal Malignancies ◽  
Upper Gastrointestinal

Abstract Background Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers. Methods A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies. Results Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively. Conclusions AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.

scholarly journals Diagnostic accuracy of circulating tumor cells detection in gastric cancer: systematic review and meta-analysis

BMC Cancer ◽  
2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Lanhua Tang ◽  
Shushan Zhao ◽  
Wei Liu ◽  
Nicholas F Parchim ◽  
Jin Huang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Meta Analysis

scholarly journals Clinical Potential of DNA Methylation in Gastric Cancer: A Meta-Analysis

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e36275 ◽  
Author(s):  
Nur Sabrina Sapari ◽  
Marie Loh ◽  
Aparna Vaithilingam ◽  
Richie Soong
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Meta Analysis ◽  
Clinical Potential

Diagnostic accuracy of magnetic resonance angiography for acute pulmonary embolism - a systematic review and meta-analysis

VASA ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 149-154 ◽  
Author(s):  
Jie Li ◽  
Lei Feng ◽  
Jiangbo Li ◽  
Jian Tang
Keyword(s):  
Pulmonary Embolism ◽  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Diagnostic Accuracy ◽  
Acute Pulmonary Embolism ◽  
Meta Analysis ◽  
Likelihood Ratios ◽  
Summary Receiver Operating Characteristic ◽  
Acute Pe ◽  
Limited Sensitivity

Abstract. Background: The aim of this meta-analysis was to evaluate the diagnostic accuracy of magnetic resonance angiography (MRA) for acute pulmonary embolism (PE). Methods: A systematic literature search was conducted that included studies from January 2000 to August 2015 using the electronic databases PubMed, Embase and Springer link. The summary receiver operating characteristic (SROC) curve, sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) as well as the 95 % confidence intervals (CIs) were calculated to evaluate the diagnostic accuracy of MRA for acute PE. Meta-disc software version 1.4 was used to analyze the data. Results: Five studies were included in this meta-analysis. The pooled sensitivity (86 %, 95 % CI: 81 % to 90 %) and specificity (99 %, 95 % CI: 98 % to 100 %) demonstrated that MRA diagnosis had limited sensitivity and high specificity in the detection of acute PE. The pooled estimate of PLR (41.64, 95 % CI: 17.97 to 96.48) and NLR (0.17, 95 % CI: 0.11 to 0.27) provided evidence for the low missed diagnosis and misdiagnosis rates of MRA for acute PE. The high diagnostic accuracy of MRA for acute PE was demonstrated by the overall DOR (456.51, 95 % CI: 178.38 - 1168.31) and SROC curves (AUC = 0.9902 ± 0.0061). Conclusions: MRA can be used for the diagnosis of acute PE. However, due to limited sensitivity, MRA cannot be used as a stand-alone test to exclude acute PE.

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