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      Diagnostic and prognostic value of circulating tumor DNA in gastric cancer: a meta-analysis

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          Abstract

          Background

          Circulating tumor DNA (ctDNA) has offered a minimally invasive approach for detection and measurement of gastric cancer (GC). However, its diagnostic and prognostic value in gastric cancer still remains unclear.

          Results

          A total of 16 studies comprising 1193 GC patients met our inclusion criteria. The pooled sensitivity and specificity were 0.62 (95% confidence intervals (CI) 0.59−0.65) and 0.95 (95% CI 0.93–0.96), respectively. The AUSROC (area under SROC) curve was 0.94 (95% CI 0.89–0.98). The results showed that the presence of certain ctDNA markers was associated with larger tumor size (OR: 0.26, 95% CI 0.11–0.61, p = 0.002), TNM stage (I + II/III + IV, OR: 0.11, 95% CI 0.07−0.17, p = 0.000), as well as H. pylori infection. ( H.p negative/ H.p positive, OR: 0.57, 95% CI 0.36–0.91, p = 0.018). Moreover, there was also a significant association between the presence of ctDNA and worse overall survival (HR 1.77, 95% CI 1.38−2.28, p < 0.001), as well as disease-free survival (HR 4.36, 95% CI 3.08−6.16, p < 0.001).

          Materials and Methods

          Pubmed, Embase, Cochrane Library and Web of Science databases were searched for relating literature published up until November 30, 2016. Diagnostic accuracy variables were pooled by the Meta-Disc software. Engauge Digitizer and Stata software were applied for prognostic data extraction and analysis.

          Conclusions

          Our meta-analysis indicates the detection of certain ctDNA targets is significantly associated with poor prognosis of GC patients, with high specificity and relatively moderate sensitivity.

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          Most cited references32

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          Measuring the accuracy of diagnostic systems.

          J Swets (1988)
          Diagnostic systems of several kinds are used to distinguish between two classes of events, essentially "signals" and "noise". For them, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy. It is the only measure available that is uninfluenced by decision biases and prior probabilities, and it places the performances of diverse systems on a common, easily interpreted scale. Representative values of this measure are reported here for systems in medical imaging, materials testing, weather forecasting, information retrieval, polygraph lie detection, and aptitude testing. Though the measure itself is sound, the values obtained from tests of diagnostic systems often require qualification because the test data on which they are based are of unsure quality. A common set of problems in testing is faced in all fields. How well these problems are handled, or can be handled in a given field, determines the degree of confidence that can be placed in a measured value of accuracy. Some fields fare much better than others.
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            Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints.

            Meta-analyses aim to provide a full and comprehensive summary of related studies which have addressed a similar question. When the studies involve time to event (survival-type) data the most appropriate statistics to use are the log hazard ratio and its variance. However, these are not always explicitly presented for each study. In this paper a number of methods of extracting estimates of these statistics in a variety of situations are presented. Use of these methods should improve the efficiency and reliability of meta-analyses of the published literature with survival-type endpoints.
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              Meta-DiSc: a software for meta-analysis of test accuracy data

              Background Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. Results Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. Conclusion Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at .
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                24 January 2017
                21 December 2016
                : 8
                : 4
                : 6330-6340
                Affiliations
                1 Chinese PLA General Hospital, Department of General Surgery, Beijing, 100853, People's Republic of China
                Author notes
                Correspondence to: Lin Chen, chenlinbj@ 123456sina.com
                Article
                14064
                10.18632/oncotarget.14064
                5351635
                28009985
                2887569a-c8fb-4e25-87f2-5797e2411973
                Copyright: © 2017 Gao et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 September 2016
                : 13 December 2016
                Categories
                Research Paper

                Oncology & Radiotherapy
                ctdna,gastric cancer,diagnosis,prognosis,meta-analysis
                Oncology & Radiotherapy
                ctdna, gastric cancer, diagnosis, prognosis, meta-analysis

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