Tutorial: statistical methods for the meta-analysis of diagnostic test accuracy studies.

This tutorial shows how to perform a meta-analysis of diagnostic test accuracy studies (DTA) based on a 2 × 2 table available for each included primary study. First, univariate methods for meta-analysis of sensitivity and specificity are presented. Then the use of univariate logistic regression models with and without random effects for e.g. sensitivity is described. Diagnostic odds ratios (DOR) are then introduced to combine sensitivity and specificity into one single measure and to assess publication bias. Finally, bivariate random effects models using the exact binomial likelihood to describe within-study variability and a normal distribution to describe between-study variability are presented as the method of choice. Based on this model summary receiver operating characteristic (sROC) curves are constructed using a regression model logit-true positive rate (TPR) over logit-false positive rate (FPR). Also it is demonstrated how to perform the necessary calculations with the freely available software R. As an example a meta-analysis of DTA studies using Procalcitonin as a diagnostic marker for sepsis is presented.

TOMAS-R: A template to identify and plan analysis for clinically important variation and multiplicity in diagnostic test accuracy systematic reviews.

The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (DTA) provides guidance on important aspects of conducting a test accuracy systematic review. In this paper we present TOMAS-R (Template of Multiplicity and Analysis in Systematic Reviews), a structured template to use in conjunction with current Cochrane DTA guidance, to help identify complexities in the review question and to assist planning of data extraction and analysis when clinically important variation and multiplicity is present. Examples of clinically important variation and multiplicity could include differences in participants, index tests and test methods, target conditions and reference standards used to define them, study design and methodological quality. Our TOMAS-R template goes beyond the broad topic headings in current guidance that are sources of potential variation and multiplicity, by providing prompts for common sources of heterogeneity encountered from our experience of authoring over 100 reviews. We provide examples from two reviews to assist users. The TOMAS-R template adds value by supplementing available guidance for DTA reviews by providing a tool to facilitate discussions between methodologists, clinicians, statisticians and patient/public team members to identify the full breadth of review question complexities early in the process. The use of a structured set of prompting questions at the important stage of writing the protocol ensures clinical relevance as a main focus of the review, while allowing identification of key clinical components for data extraction and later analysis thereby facilitating a more efficient review process.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Performance of Leukocyte Esterase Reagent Strips in the Detection of Spontaneous Bacterial Peritonitis in Cirrhotic Patients: A Systematic Review and Meta-analysis.

Background: Spontaneous bacterial peritonitis (SBP) is a bacterial infection associated with a high mortality rate in cirrhotic patients. The gold standard for the detection of SBP is a manual cell count from ascitic fluid; however, alternative screening methods are under investigation. In particular, leukocyte esterase reagent strips (LERS) has been studied as an alternative method to detect SBP with a low cost and instant turnaround time. Therefore, this study aims to evaluate the performance of LERS in the detection of SBP. Methods: A literature search was performed for studies evaluating LERS for the detection of SBP on PubMed, Embase, Scopus, Cochrane, and clinical trial registries. Summary sensitivity, specificity, log diagnostic odds ratio (LDOR), and the area under the summary receiver operating curve (AUC) were calculated according to the respective manufacturer. Results: In total, 31 studies were evaluated. The summary sensitivity of Aution Sticks, Combur, Multistix, Periscreen reagent strips was 0.962 (95% confidence interval [CI] 0.926, 0.998), 0.892 (95% CI 0.846, 0.938), 0.806 (95% CI 0.738, 0.874), and 0.939 (95% CI 0.900, 0.979), respectively. The summary specificity of Aution Sticks, Combur, Multistix, and Periscreen reagent strips was 0.940 (95% CI 0.904, 0.976), 0.922 (95% CI 0.874, 0.970), 0.974 (95% CI 0.962, 0.985), and 0.672 (95% CI 0.381, 0.963), respectively. Conclusion: LERS appears to have a notable overall performance for the detection of SBP. LERS appeared to be an acceptable alternative to diagnose SBP in facilities without ability to perform cell count. However, there were significant differences in performance between each manufacturer.

Meta-Analysis Methods of Diagnostic Test Accuracy Studies.

Meta-analytic techniques are used to combine the results of different studies that have evaluated the accuracy of diagnostic tests. In this article, we present univariate and multivariate meta-analysis methods for a single test and we provide an extensive description of methods for meta-analysis and comparison of multiple diagnostic tests. We close with a practical example of a meta-analysis that aimed to determine whether Rheumatoid Factor identifies patients with Rheumatoid Arthritis.

Diagnostic accuracy of screening algorithms to identify persons with active pulmonary tuberculosis at prison entry: protocol of a systematic review and network meta-analysis.

Prison inmates are a high-risk group for tuberculosis (TB) infection and disease due to the increasing number of vulnerable fringe groups, risk factors (e.g., alcohol and drug addictions), contagious diseases (HIV, hepatitis), and their high-risk behavior. Compared to the general population, TB incidence and prevalence rates are significantly higher among prison inmates. Early identification of potentially infectious pulmonary TB (PTB) and targeted care of sick inmates are essential to effectively control TB within the prison system. The WHO recommends combining active and passive case-finding in prisons. No study has been published comparing the broad spectrum of screening tools using a diagnostic accuracy network meta-analysis (NMA). We aim to identify the most accurate TB case-finding algorithm at prison entry that is feasible in resource-limited prisons of high-burden TB countries and ensures continuous comprehensive TB detection services in such settings. Evidence generated by this NMA can provide important decision support in selecting the most (cost-) effective algorithms for screening methods for resource-limited settings in the short, medium, and long terms.

Identification of the susceptibility genes for COVID-19 in lung adenocarcinoma with global data and biological computation methods.

INTRODUCTION: The risk of infection with COVID-19 is high in lung adenocarcinoma (LUAD) patients, and there is a dearth of studies on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. OBJECTIVES: To fill the research void on the molecular mechanism underlying the high susceptibility of LUAD patients to COVID-19 from the perspective of the global differential expression landscape. METHODS: Herein, we identified genes, specifically the differentially expressed genes (DEGs), correlated with the susceptibility of LUAD patients to COVID-19. These were obtained by calculating standard mean deviation (SMD) values for 49 SARS-CoV-2-infected LUAD samples and 24 non-affected LUAD samples, as well as 3931 LUAD samples and 3027 non-cancer lung samples from 40 pooled RNA-seq and microarray datasets. Hub susceptibility genes significantly related to COVID-19 were further selected by weighted gene co-expression network analysis. Then, the hub genes were further analyzed via an examination of their clinical significance in multiple datasets, a correlation analysis of the immune cell infiltration level, and their interactions with the interactome sets of the A549 cell line. RESULTS: A total of 257 susceptibility genes were identified, and these genes were associated with RNA splicing, mitochondrial functions, and proteasomes. Ten genes, MEA1, MRPL24, PPIH, EBNA1BP2, MRTO4, RABEPK, TRMT112, PFDN2, PFDN6, and NDUFS3, were confirmed to be the hub susceptibility genes for COVID-19 in LUAD patients, and the hub susceptibility genes were significantly correlated with the infiltration of multiple immune cells. CONCLUSION: In conclusion, the susceptibility genes for COVID-19 in LUAD patients discovered in this study may increase our understanding of the high risk of COVID-19 in LUAD patients.

A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests.

For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test, as well as the parameters of the summary receiver operator characteristic (SROC) curve, along with a comparison between the SROCs of each test. Our methodology is demonstrated with an extensive simulation study and illustrated by meta-analysing two examples where two tests for the diagnosis of a particular disease are compared. Our study suggests that there can be an improvement on GLMM in fit to data since our model can also provide tail dependencies and asymmetries.

Exosomal miR-1246 in body fluids is a potential biomarker for gastrointestinal cancer.

AIM: The aim was to systematically evaluate whether exosomal miRNAs could be regarded as potential minimally invasive biomarkers of diagnosis for gastrointestinal cancer. METHODS: A systematic review and meta analysis of exosomal miRNA expression in gastrointestinal cancer were performed. RESULTS: A total of 370 articles were retrieved from PubMed and EMBASE. The summary receiver operating characteristic curves of three miRNAs (miR-21, miR-1246 and miR-4644) were drawn, miR-21, miR-1246 and miR-4644 exhibited sensitivities of 0.66, 0.920 and 0.750, respectively; specificities were 0.87, 0.958 and 0.769, respectively; and areas under the curve for discriminating gastrointestinal cancer patients from control subjects were 0.876, 0.969 and 0.827, respectively. CONCLUSION: Exosome miR-1246 had the highest level of diagnostic efficiency, which indicated that miR-1246 could be a biomarker.

Clinically relevant circulating microRNA profiling studies in pancreatic cancer using meta-analysis.

BACKGROUND: Pancreatic cancer (PaCa) is the most lethal gastrointestinal (GI) tumor. Although many studies on differentially expressed miRNAs as candidate biomarkers of pancreatic cancer have been published, reliability of these findings generated from investigations performed in single laboratory settings remain unclear. RESULTS: There were 29 articles with a total of 2,225 patients and 1,618 controls included in this meta-analysis. The pooled sensitivity was 82% (95% CI, 79-85%); the specificity was 85% (95% CI, 79-89%); and area under the curve (AUC) was 0.89 (95% CI, 0.86-0.92). Subgroup analyses indicated that there were significant divergences between Caucasian and Asian subgroups for circulating miRNA analysis. MATERIALS AND METHODS: To comprehensively investigate the potential utility of miRNAs as biomarkers of the disease, we searched publications diagnosing PaCa using miRNAs from PubMed, Medline, Embase, Google Scholar and Chinese National Knowledge Infrastructure (CNKI) databases. The sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic (SROC) curve were used to examine the overall test performance, and heterogeneity was analyzed with the I2 test. CONCLUSIONS: Our analysis demonstrated that multiple miRNAs (SEN: 85%; SPE: 89%; AUC: 0.93) were more accurate for diagnosing PaCa than a single miRNA (SEN: 78%; SPE: 79%; AUC: 0.84), and future studies are still needed to confirm the diagnostic value of these pooled miRNAs for PaCa.

A multivariate method for meta-analysis and comparison of diagnostic tests.

We present here an extension of the classic bivariate random effects meta-analysis for the log-transformed sensitivity and specificity that can be applied for two or more diagnostic tests. The advantage of this method is that a closed-form expression is derived for the calculation of the within-studies covariances. The method allows the direct calculation of sensitivity and specificity, as well as, the diagnostic odds ratio, the area under curve and the parameters of the summary receiver operator's characteristic curve, along with the means for a formal comparison of these quantities for different tests. There is no need for individual patient data or the simultaneous evaluation of both diagnostic tests in all studies. The method is simple and fast; it can be extended for several diagnostic tests and can be fitted in nearly all statistical packages. The method was evaluated in simulations and applied in a meta-analysis for the comparison of anti-cyclic citrullinated peptide antibody and rheumatoid factor for discriminating patients with rheumatoid arthritis, with encouraging results. Simulations suggest that the method is robust and more powerful compared with the standard bivariate approach that ignores the correlation between tests. Copyright © 2016 John Wiley & Sons, Ltd.

Accuracy of serum interleukin (IL)-6 in sepsis diagnosis: a systematic review and meta-analysis.

OBJECTIVE: To systematic review and estimate the accuracy of Interleukin 6 assay in the diagnosis of sepsis by meta-analysis. METHODS: With the aim to confirm this correlation, this paper performed a meta-analysis of 6 studies and the Sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with corresponding 95% confidence intervals (CI) of each study were calculated and the pooled sensitivity was calculate using Random Effects Model and Summary receiver operating characteristic curves were constructed. RESULTS: The pooled sensitivity for the diagnosis of sepsis was 80% (95% CI, 77% to 83%) and the specificity of 85% (95% CI, 81% to 88%). For sepsis versus health or infection, the area under the curve was 0.868. In neonate subgroup, IL-6 had a pooled sensitivity of 77.0% (95% CI, 73.0% to 81.0%) and specificity of 91.0% (95% CI, 86.0% to 94.0%) for sepsis diagnosis. In adult, IL-6 had a pooled sensitivity of 85.0% (95% CI, 80.0% to 88.0%) and specificity of 62.0% (95% CI, 55.0% to 68.0%) to identify sepsis. The AUC was 81.0%, and Q was 0.74. CONCLUSIONS: IL6 is a highly accurate diagnostic modality for the identification of sepsis, with promise for integration into routine imaging protocols for thyroid nodules.

Motility modulation by the small non-coding RNA SroC in Salmonella Typhimurium.

Bacterial regulatory networks of gene expression include the interaction of diverse types of molecules such as the small non-coding RNAs (sRNAs) and their cognate messenger RNAs (mRNAs). In this study, we demonstrated that the Salmonella Typhimurium sRNA SroC is significantly expressed between the late-exponential and stationary phase of growth in an rpoS-dependent manner. The expression of flagellar genes predicted as targets of this sRNA was quantitatively analyzed in both a ΔsroC mutant and a SroC-overexpressing (pSroC) strain. Deletion of sroC increased flagellar gene expression (i.e. flhBAE and fliE). Conversely, overexpression of SroC reduced flhBAE and fliE expression. These observations correlated with phenotypic evaluation of motility, where sroC deletion slightly increased motility, which in turn, was drastically reduced upon overexpression of SroC. The effects of deletion and overexpression of sroC in biofilm formation were also examined, where the ΔsroC and pSroC strains exhibited a reduced and increased ability to form biofilm, respectively. Furthermore, electron microscopy revealed that the wild-type strain overexpressing SroC had a non-flagellated phenotype. Taken together, our results showed that S. Typhimurium sRNA SroC modulates the flagellar synthesis by down-regulating the expression of flhBAE and fliE genes.

A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution.

Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower specificities. A generalized linear mixed model (GLMM) is currently recommended to synthesize diagnostic test accuracy studies. We propose a copula mixed model for bivariate meta-analysis of diagnostic test accuracy studies. Our general model includes the GLMM as a special case and can also operate on the original scale of sensitivity and specificity. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different characterizations of the bivariate random effects distribution. Our general methodology is demonstrated with an extensive simulation study and illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models. Our modelling framework is implemented in the package CopulaREMADA within the open source statistical environment R.

Performance of transient elastography assessing fibrosis of single hepatitis B virus infection: a systematic review and meta-analysis of a diagnostic test.

INTRODUCTION: Transient elastography has good diagnostic accuracy for predicting fibrosis and cirrhosis related to hepatitis B virus. However, the enrolled subjects in some studies have also included chronic hepatitis B patients with chronic hepatitis C coinfection or other etiologies of liver diseases. This might under- or overestimate the role of transient elastography in chronic hepatitis B patients. METHODS: A systematic review was conducted on records in PubMed, EMBASE and the Cochrane Library electronic databases until 31 December 2013. We systematically assessed the effectiveness and accuracy of transient elastography for predicting the fibrosis and cirrhosis of the single hepatitis B virus. The questionnaire for quality assessment of diagnostic accuracy studies was used. RESULTS: In this meta-analysis, out of 257 articles evaluated for eligibility, 165 satisfied the predetermined inclusion criteria for meta-analysis. Eventually, our final data set for the meta-analysis contained 19 studies. The areas under the summary receiver-operating characteristic curve for transient elastography of significant fibrosis and cirrhosis were 0.8225 and 0.9108, respectively. The pooled diagnostic odds ratios for transient elastography of significant fibrosis and cirrhosis were 11.19 (95 % CI 6.63-18.89) and 26.87 (95 % CI 17.88-40.38), respectively. In Europe, the areas of significant fibrosis and cirrhosis were 0.803 and 0.905; those in Asia were 0.871 and 0.914, respectively. CONCLUSIONS: Based on this meta-analysis we claim that liver stiffness measurement using transient elastography has good diagnostic accuracy for predicting single hepatitis B virus-related significant fibrosis and cirrhosis, especially in Asian populations.

Cross talk between ABC transporter mRNAs via a target mRNA-derived sponge of the GcvB small RNA.

There is an expanding list of examples by which one mRNA can posttranscriptionally influence the expression of others. This can involve RNA sponges that sequester regulatory RNAs of mRNAs in the same regulon, but the underlying molecular mechanism of such mRNA cross talk remains little understood. Here, we report sponge-mediated mRNA cross talk in the posttranscriptional network of GcvB, a conserved Hfq-dependent small RNA with one of the largest regulons known in bacteria. We show that mRNA decay from the gltIJKL locus encoding an amino acid ABC transporter generates a stable fragment (SroC) that base-pairs with GcvB. This interaction triggers the degradation of GcvB by RNase E, alleviating the GcvB-mediated mRNA repression of other amino acid-related transport and metabolic genes. Intriguingly, since the gltIJKL mRNA itself is a target of GcvB, the SroC sponge seems to enable both an internal feed-forward loop to activate its parental mRNA in cis and activation of many trans-encoded mRNAs in the same pathway. Disabling this mRNA cross talk affects bacterial growth when peptides are the sole carbon and nitrogen sources.

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

OBJECTIVES: Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. STUDY DESIGN AND SETTING: Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. RESULTS: The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. CONCLUSION: The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present.

Accuracy of Rapid Point-of-Care Diagnostic Tests for Hepatitis B Surface Antigen-A Systematic Review and Meta-analysis.

BACKGROUND: Rapid point-of-care tests provide plausible diagnostic strategy for hepatitis B surface antigen (HBsAg) in low resource areas. However, their utility depends upon their overall performance. Our objective was to meta-analyze the diagnostic accuracy of rapid point-of-care tests for HBsAg. METHODS: Literature search was done with the help of a metasearch engine Metta, a query interface for retrieving articles from five leading medical databases. Studies that employed rapid point-of-care tests for detection of HBsAg and compared the results with reference test were included. Two reviewers performed quality assessment of the studies and extracted data for estimating test accuracy. Twenty-seven studies were meta-analyzed and stratified by multiple parameters. RESULTS: Twenty-seven studies had evaluated 49 test brands and generated 76 data points. Sensitivity of individual tests varied widely and were heterogeneous (range 43.5%-99.8%); while specificity estimates were more robust and close to 100% (range 90%-100%). Overall pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR and diagnostic odds ratio for all tests were 97.1% (95% CI, 96.1%-97.9%), 99.9% (CI, 99.8%-100%), 118.4 (CI, 84.7-165.5), 0.032 (CI, 0.023-0.045) and 4094.7 (CI, 2504.1-6600.8) respectively. This suggested high pooled accuracy for all studies. We found substantial heterogeneity between studies. Three factors (study location, reference standard and study score) appeared most strongly associated with test estimates and observed heterogeneity. The Determine test showed consistency in performance in studies done across developed and developing countries and the Determine and the BinaxNOW tests had significantly higher estimates than pooled estimates of remaining tests. Tests revealed analytical sensitivity of 4 IU/ml against manufacturer's claim of 0.5 IU/ml; reduced sensitivity with HBsAg mutants and poor performance in seroconversion panels. CONCLUSIONS: Tests with better analytical sensitivity need to be developed and their feasibility and outcomes in various clinical settings need to be addressed.

Systematic reviews in the telomerase activity testing for screening lung cancer by use of SROC curve method / 实用肿瘤学杂志

Objective To evaluate the clinical significance of telomerase in the diagnosis of lung cancerusing SROC curve method.Methods Looking“telomerase”and“lung cancer”as keywords,retrieving journalspublished within past 20 years in order to incorporate into literatures and to collect data .To conduct the SROC analysisusing Meta -DiSc 1.4 software.Results 1.Twenty -two documents were sampled as tissue specimensand the heterogeneity was relatively large (P =0.017).To analyze the data with random effective models ,thecombined sensitivity and specificity were 0.788(0.761 -0.814)and 0.955(0.936 -0.969),respectively.TheSROC AUC area under the curve was 0.9515,SE(AUC) =0.0145.2.There was no heterogeneity(P =0.633)amongthe 10 lavaged literatures.By use of fixed effects model for data analysis ,the combined sensitivity and specificitywere 0.777(0.734 -0.816) and 0.922(0.888 -0.948),and SROC AUC area under the curve was0.9369,SE(AUC) =0.0141.Conclusion Telomerase is a ideal tumor marker,and the detection of telomeraseactivity in lavage fluid is stable and accurate in clinical diagnosis .

Diagnostic value of bladder tumor fibronectin in patients with bladder tumor: a systematic review with meta-analysis.

OBJECTIVES: Bladder tumor fibronectin (BTF) has been related as a promising biomarker for the early diagnosis of bladder tumor. The meta-analysis was used to establish the diagnostic value of bladder tumor fibronectin in diagnosing bladder tumor. METHODS: Relevant literatures evaluating the value of BTF in the diagnosis of bladder tumor were searched in PubMed, Embase, China National Knowledge Infrastructure (CNKI), Technology of Chongqing (VIP), and Wan Fang Data. Summary estimates were used to evaluate the value of BTF in the diagnosis of bladder tumor by using the Meta-DiSc and STATA 11.0 statistical software. RESULTS: The meta-analysis included 5 studies (649 patients, 291 controls). The summary estimates revealed that the pooled sensitivity was 81% (95% confidence interval [CI]: 74%-85.1%) and specificity was 80% (95%CI 74%-84%). In addition, the area under the summary ROC curve (AUC) was 0.86 (95%CI 0.82-0.89). CONCLUSION: BTF is a potential marker for the diagnosis of bladder tumor, and more prospective studies are needed in the future.