Meta-analysis Of Diagnostic Accuracy Studies Research Articles

Handgrip Strength: An Early Warning Sign for Mobility Decline? A Meta-Analysis of Diagnostic Accuracy Studies

Background: Mobility limitations significantly impact the quality of life of older adults. Early identification of individuals at risk is crucial for timely intervention. This meta-analysis investigates the diagnostic accuracy of handgrip strength (HGS) in predicting future mobility decline in older adults. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted for diagnostic accuracy studies published between 2018 and 2024, evaluating the ability of baseline HGS to predict incident mobility limitations in older adults (≥60 years). Mobility limitations were defined as difficulties in performing activities of daily living (ADLs) or instrumental activities of daily living (IADLs). The primary outcomes were sensitivity, specificity, and diagnostic odds ratio (DOR) of HGS for predicting mobility decline. A bivariate random-effects model was used to pool data. Results: Seven diagnostic studies with a total of 3,870 participants were included. The pooled sensitivity of HGS for predicting mobility decline was 0.72 (95% CI: 0.65-0.78), and the pooled specificity was 0.70 (95% CI: 0.66-0.74). The pooled DOR was 4.85 (95% CI: 3.21-7.32), indicating good discriminatory ability. Conclusion: This meta-analysis demonstrates that HGS has moderate sensitivity and specificity for predicting future mobility decline in older adults. HGS assessment can be a valuable tool for identifying individuals at risk, although further research is needed to determine optimal cut-off points and combine HGS with other risk factors for improved prediction.

Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study.

The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf etal., Biometrical Journal. 2021; 63(4): 699-711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.

Ancillary immunohistochemistry testing for loss of p16 in melanoma: A systematic review and meta-analysis of diagnostic accuracy studies.

Ancillary immunohistochemistry testing for p16 loss has been proposed as a diagnostic tool for melanoma, but its accuracy remains uncertain. A systematic review and meta-analysis were conducted on 26 studies involving 979 melanomas and 974 nevi. Through bivariate analysis of data across all cut-off values, the sensitivity and specificity were calculated to be 0.55 (95% confidence interval [CI]: 0.38, 0.70) and 0.85 (95% CI: 0.70, 0.94), respectively. Summary estimates of diagnostic accuracy fell below recommended thresholds for effective tests, but subgroup analysis suggested that p16 loss could aid in diagnosing ambiguous lesions as melanoma in certain scenarios. However, the presence of p16 expression in these contexts does not definitively rule out melanoma. The findings were limited by underpowered exploratory study designs at risk for bias in patient selection and test interpretation. While the use of p16 immunohistochemistry for detecting melanoma is not universally reliable, it may serve as a confirmatory test in differential diagnoses involving common, congenital, acral, Spitz, and deep penetrating nevi. Nevertheless, further studies are needed to validate its utility. Until then, the application of p16 immunohistochemistry in melanoma diagnosis should be regarded as experimental.

Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.

Pulmonary arterial hypertension (PAH) is a severe cardiovascular condition characterized by pulmonary vascular remodeling, increased resistance to blood flow, and eventual right heart failure. Right heart catheterization (RHC) is the gold standard diagnostic technique, but due to its invasiveness, it poses risks such as vessel and valve injury. In recent years, machine learning (ML) technologies have offered non-invasive alternatives combined with ML for improving the diagnosis of PAH. The study aimed to evaluate the diagnostic performance of various methods, such as electrocardiography (ECG), echocardiography, blood biomarkers, microRNA, chest x-ray, clinical codes, computed tomography (CT) scan, and magnetic resonance imaging (MRI), combined with ML in diagnosing PAH. The outcomes of interest included sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). This study employed the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for quality appraisal and STATA V.12.0 for the meta-analysis. A comprehensive search across six databases resulted in 26 articles for examination. Twelve articles were categorized as low-risk, nine as moderate-risk, and five as high-risk. The overall diagnostic performance analysis demonstrated significant findings, with sensitivity at 81% (95% CI = 0.76-0.85, p < 0.001), specificity at 84% (95% CI = 0.77-0.88, p < 0.001), and an AUC of 89% (95% CI = 0.85-0.91). In the subgroup analysis, echocardiography displayed outstanding results, with a sensitivity value of 83% (95% CI = 0.72-0.91), specificity value of 93% (95% CI = 0.89-0.96), PLR value of 12.4 (95% CI = 6.8-22.9), and DOR value of 70 (95% CI = 23-231). ECG demonstrated excellent accuracy performance, with a sensitivity of 82% (95% CI = 0.80-0.84) and a specificity of 82% (95% CI = 0.78-0.84). Moreover, blood biomarkers exhibited the highest NLR value of 0.50 (95% CI = 0.42-0.59). The implementation of echocardiography and ECG with ML for diagnosing PAH presents a promising alternative to RHC. This approach shows potential, as it achieves excellent diagnostic parameters, offering hope for more accessible and less invasive diagnostic methods. PROSPERO (CRD42024496569).

Evidence for differences in patterns of temporal trends in meta-analyses of diagnostic accuracy studies in the Cochrane database of systematic reviews

ObjectivesTemporal trends in comparative meta-analyses of interventions are well-recognized in the medical literature. For studies of diagnostic test accuracy (DTA), evidence of temporal trends is growing and the importance of assessing and reporting them has been highlighted in recent guidelines on postmarket surveillance in several jurisdictions. In this study, we evaluate the prevalence and patterns of time trends using a larger and more up-to-date set of DTA systematic reviews than has previously been examined, from the Cochrane Database of Systematic Reviews. Study Design and SettingCumulative meta-analysis was conducted on bivariate random effects meta-analysis estimates of sensitivity and specificity, after ranking studies by publication date. Trends for all studies were assessed graphically using plots of summary estimates by study rank, and using receiver operating characteristic plots of sensitivity vs specificity. Linear trends were also described using weighted linear regression with autocorrelated errors of summary estimates against study rank. Various patterns of nonlinear trends were characterized descriptively. ResultsThe analysis included 46 reviews (92 meta-analyses) conducted between 2017 and 2022. The total number of studies within all reviews was 1486, with a median (IQR) 7134 (2782–16,406) participants per review. Reviews had a median (IQR) time span of 19 (15-25) publication years. Time trends in at least 1 DTA measure were observed in 40 (87%) reviews, and statistically significant linear trends in 32 (70%) reviews. Nonlinear time trends were observed in 14 (30%) reviews. There was no evidence for a trend in either DTA measure in 6 (13%) reviews. ConclusionThe study contributes evidence on the variety in patterns of linear and nonlinear temporal DTA trends which has not previously been described. We recommended researchers check statistical assumptions of trend analysis methods, eg, using graphical methods. Further research into potential reasons for time trends could contribute to the robustness of future meta-analyses.

Hierarchical multinomial processing tree models for meta-analysis of diagnostic accuracy studies

Abstract Meta-analysis represents a widely accepted approach for evaluating the accuracy of diagnostic tools in clinical and psychological investigations. This article investigates the applicability of multinomial tree models recently suggested in the literature under a fixed-effects formulation for assessing the accuracy of binary classification tools, where the study specific disease prevalences are taken into account. The model proposed in this article extends previous results to a hierarchical structure accounting for the variability between the studies included in the meta-analysis. Interestingly, by exploiting the parameter separability of the complete likelihood function, the resulting hierarchical multinomial tree model is shown to coincide, in its interest parameter component, with the well-known bivariate random-effects model under an exact within-study distribution for the number of true positives and true negatives subjects. The proposal is in line with a latent-trait approach, where inference follows a frequentist point of view. The applicability of the proposed model and its performance with respect to its fixed-effects counterpart and to the approximate bivariate random-effects model based on normality assumptions commonly used in the literature is evaluated in a series of simulation studies. Methods are applied to a real meta-analysis about the accuracy of the confusion assessment method as delirium screening tool.

Disguised incorporation bias and meta-analysis of diagnostic accuracy studies.

Disguised incorporation bias and meta-analysis of diagnostic accuracy studies.

Approximate likelihood and pseudo-likelihood inference in meta-analysis of diagnostic accuracy studies accounting for disease prevalence and study design.

Bivariate random-effects models represent a recommended approach for meta-analysis of diagnostic test accuracy, jointly modeling study-specific sensitivity and specificity. As the severity of the disease status can vary across studies, a proper analysis should account for the dependence of the accuracy measures on the disease prevalence. To this aim, trivariate generalized linear mixed-effects models have been proposed in the literature, although computational difficulties strongly limit their applicability. In addition, the attention has been mainly paid to cohort studies, where the study-specific disease prevalence can be estimated from, while information from case-control studies is often neglected. To overcome such limits, this article introduces a trivariate approximate normal model, which accounts for disease prevalence along with accuracy measures in cohort studies and sensitivity and specificity in case-control studies. The model represents an extension of the bivariate normal mixed-effects model originally developed for meta-analysis not accounting for disease prevalence, under an approximate normal within-study distribution for the logit of estimated sensitivity and specificity. The components of the approximate within-study covariance matrix are derived and the likelihood function is obtained in closed-form. The approximate likelihood approach is compared to that based on the exact within-study distribution and to its modifications following a pseudo-likelihood strategy aimed at reducing the computational effort. The comparison is based on simulation studies in a variety of scenarios, and illustrated in a meta-analysis about the accuracy of a test to diagnose fungal infection and a meta-analysis of a noninvasive test to detect colorectal cancer.

Simulation study to: A systematic comparison of different approaches for meta-analysis of diagnostic accuracy studies with multiple thresholds

Simulation study to: A systematic comparison of different approaches for meta-analysis of diagnostic accuracy studies with multiple thresholds

A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves

Bivariate random-effects models represent an established approach for meta-analysis of accuracy measures of a diagnostic test, which are typically given by sensitivity and specificity. A recent formulation of the classical model describes the test accuracy in terms of study-specific Receiver Operating Characteristics curves. In this way, the resulting summary curve can be thought of as an average of the study-specific Receiver Operating Characteristics curves. Within this framework, the paper shows that the standard likelihood approach for inference is prone to several issues. Small sample size can give rise to unreliable conclusions and convergence problems deeply affect the analysis. The proposed alternative is a simulation-extrapolation method, called SIMEX, developed within the measurement error literature. It suits the meta-analysis framework, as the accuracy measures provided by the studies are estimates rather than true values, and thus are prone to error. The methods are compared in a series of simulation studies, covering different scenarios of interest, including deviations from normality assumptions. SIMEX reveals a satisfactory strategy, providing more accurate inferential results if compared to the likelihood approach, while avoiding convergence failure. The approaches are applied to a meta-analysis of the accuracy of the ultrasound exam for diagnosing abdominal tuberculosis in HIV-positive subjects.

A semiparametric approach for meta-analysis of diagnostic accuracy studies with multiple cut-offs.

The accuracy of a diagnostic test is often expressed using a pair of measures: sensitivity (proportion of test positives among all individuals with target condition) and specificity (proportion of test negatives among all individuals without target condition). If the outcome of a diagnostic test is binary, results from different studies can easily be summarized in a meta-analysis. However, if the diagnostic test is based on a discrete or continuous measure (e.g., a biomarker), several cut-offs within one study as well as among different studies are published. Instead of taking all information of the cut-offs into account in the meta-analysis, a single cut-off per study is often selected arbitrarily for the analysis, even though there are statistical methods for the incorporation of several cut-offs. For these methods, distributional assumptions have to be met and/or the models may not converge when specific data structures occur. We propose a semiparametric approach to overcome both problems. Our simulation study shows that the diagnostic accuracy is under-estimated, although this underestimation in sensitivity and specificity is relatively small. The comparative approach of Steinhauser et al. is better in terms of coverage probability, but may lead to convergence problems. In addition to the simulation results, we illustrate the application of the semiparametric approach using a published meta-analysis for a diagnostic test differentiating between bacterial and viral meningitis in children.

Accuracy of 2D and point shear wave elastography-based measurements for diagnosis of esophageal varices: a systematic review and meta-analysis.

PURPOSE The aim of this meta-analysis is to summarize the diagnostic accuracies of point shear wave elas- tography (pSWE) and two-dimensional (2D) SWE for esophageal varices (EV) and varices needing treatment (VNT). METHODS We conducted a systematic review and meta-analysis of diagnostic accuracy studies. We searched for studies reporting the EV and VNT diagnostic accuracy of pSWE and 2D SWE using PubMed Cen- tral, SCOPUS, MEDLINE, Embase, and Cochrane databases. STATA software"Midas"package was used for meta-analysis. RESULTS A total of 24 studies with 3867 patients were included in the review. Pooled score sensitivities of pSWE were 91% (95% CI, 80%-96%) for EV, and 94% (95% CI, 86%-97%) for VNT. Pooled score sensi- tivities of 2D SWE were 78% (95% CI, 69%-85%) for EV, and 79% (95% CI, 72%-85%) for VNT. Pooled score specificities of pSWE were 70% (95% CI, 60%-78%) for EV, and 59% (95% CI, 40%-75%) for VNT. Pooled score specificities of 2D SWE for EV were 79% (95% CI, 72%-85%) 72% (95% CI, 66%-77%) for VNT. We found significant heterogeneity for all the elastography-based measurements with the chi- square test results and an I2 statistic >75%. CONCLUSION Both pSWE and 2D SWE can diagnose EV and VNT with moderate diagnostic accuracy. Further large- scale setting-specific longitudinal studies are required to establish the best modality.

Cost-effectiveness and value of information analysis of NephroCheck and NGAL tests compared to standard care for the diagnosis of acute kidney injury

BackgroundEarly and accurate acute kidney injury (AKI) detection may improve patient outcomes and reduce health service costs. This study evaluates the diagnostic accuracy and cost-effectiveness of NephroCheck and NGAL (urine and plasma) biomarker tests used alongside standard care, compared with standard care to detect AKI in hospitalised UK adults.MethodsA 90-day decision tree and lifetime Markov cohort model predicted costs, quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) from a UK NHS perspective. Test accuracy was informed by a meta-analysis of diagnostic accuracy studies. Clinical trial and observational data informed the link between AKI and health outcomes, health state probabilities, costs and utilities. Value of information (VOI) analysis informed future research priorities.ResultsUnder base case assumptions, the biomarker tests were not cost-effective with ICERs of £105,965 (NephroCheck), £539,041 (NGAL urine BioPorto), £633,846 (NGAL plasma BioPorto) and £725,061 (NGAL urine ARCHITECT) per QALY gained compared to standard care. Results were uncertain, due to limited trial data, with probabilities of cost-effectiveness at £20,000 per QALY ranging from 0 to 99% and 0 to 56% for NephroCheck and NGAL tests respectively. The expected value of perfect information (EVPI) was £66 M, which demonstrated that additional research to resolve decision uncertainty is worthwhile.ConclusionsCurrent evidence is inadequate to support the cost-effectiveness of general use of biomarker tests. Future research evaluating the clinical and cost-effectiveness of test guided implementation of protective care bundles is necessary. Improving the evidence base around the impact of tests on AKI staging, and of AKI staging on clinical outcomes would have the greatest impact on reducing decision uncertainty.

Does respiratory variation in inferior vena cava diameter predict fluid responsiveness in adult patients? A systematic review and meta-analysis of diagnostic accuracy studies

Objectives: To systematically review the diagnostic utility of the respiratory variation of the inferior vena cava diameter measured using ultrasonography for predicting fluid responsiveness in adult patients and compare the three commonly used equations, inferior vena cava distensibility, inferior vena cava collapsibility and inferior vena cava variability. Methods: We searched PubMed, Scopus, Web of Science and Cochrane library, and included studies investigating the diagnostic accuracy of the respiratory variation of the inferior vena cava measured using ultrasonography compared to a reference standard for measuring cardiac output after a fluid challenge for fluid responsiveness, and stratified participants as fluid responsive or not. We included studies conducted in the emergency department or intensive care unit. We excluded studies on paediatric, prehospital, cancer, pregnant, dialysis patients or healthy volunteers. Results: We retrieved 270 records and excluded 171 because of irrelevance, patient population or publication type. We screened the abstracts of 99 studies and then the full texts of 42 studies. Overall, 21 studies with 1321 patients were included, of whom 689 (52%) were fluid responsive. The mean threshold value for positive inferior vena cava distensibility, inferior vena cava collapsibility and inferior vena cava variability was 17%, 35% and 12%, respectively. The heterogeneity between studies was high. Bivariate diagnostic random-effects meta-analysis was used to calculate the summary receiver operating characteristics curves. The overall accuracy, sensitivity and specificity of respiratory variation of the inferior vena cava diameter were 0.85, 0.72 and 0.81, respectively. The accuracy of inferior vena cava distensibility and inferior vena cava collapsibility was similar. The diagnostic utility of respiratory variation of the inferior vena cava diameter was lower but not statistically significant in mechanically ventilated patients compared with spontaneous breathing for predicting fluid responsiveness. Conclusion: The respiratory variation of the inferior vena cava diameter has moderate diagnostic utility for predicting fluid responsiveness independent of the equation used.

Utility of the DECAF score for predicting survival of patients with COPD: a meta-analysis of diagnostic accuracy studies.

The DECAF (Dyspnea, Eosinopenia, Consolidation, Acidemia, Atrial Fibrillation) score is a widely used system for predicting the survival of patients with acute aggravation of chronic obstructive pulmonary disease (COPD). Evaluations of the predictive accuracy of DECAF have shown differing results. We performed this meta-analysis to evaluate the DECAF score as a survival predictor in patients with COPD. We have included the studies examining the accuracy of DECAF scoring system as index test with occurrence of events (mortality and need for invasive/non-invasive ventilation) as reference standards irrespective of the study design employed, type of participants and severity of the condition. We conducted a systematic search for all studies reporting the predictive accuracy of DECAF scores in the databases of PubMed Central, Scopus, Medline, Embase, and Cochrane from inception until September 2020. We have used the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool to evaluate the risk of bias. We used the STATA software "midas" package to perform the meta-analysis. We included 21 studies with 6429 patients. Most studies included were prospective. Most studies were conducted in the United Kingdom. Most studies used a cut-off value of the DECAF score ≥3 to predict the in-hospital or 30-day mortality and need for mechanical ventilation. All the studies used the occurrence of in-hospital/30-day mortality or patient undergoing mechanical ventilation as the reference standards. The pooled sensitivity and specificity of the DECAF score for predicting in-hospital mortality among patients with acute exacerbation of COPD were 74% (95% CI, 67%-79%) and 76% (95% CI, 68%-82%), respectively; and those for the 30-day mortality were 72% (95% CI, 59%-82%) and 83% (95% CI, 67%-93%), respectively. The overall quality of the studies in our meta-analysis was high. We found no significant publication biases as per Deek's test and funnel plot. This review has certain strengths. It is the first meta-analysis assessing the predictive utility of the DECAF score for in-hospital mortality among patients with AECOPD. Most studies included were of high quality according to the QUADAS-2 tool. Despite these strengths, our review had some limitations. We found a significant between-study variability in our analysis that can limit its value for inferring or interpreting the pooled findings. The predictive accuracy of the scoring system depends on many factors such as the ethnicity of the participants or patients, the timing of the scoring system assessment, and the AECOPD severity. We could not assess the influence of these variables in our study. Despite these shortcomings, our findings provide valuable information and important implications for the clinical practice involving patients with AECOPD. We found that the DECAF score can predict in-hospital and 30-day mortalities with satisfactory sensitivity and specificity.

Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies

–The area under the curve (AUC) of summary receiver operating characteristic (SROC) curve is a primary statistical outcome for meta-analysis of diagnostic test accuracy studies (DTA). However, its confidence interval has not been reported in most of DTA meta-analyses, because no certain methods and statistical packages have been provided. In this article, we provide a bootstrap algorithm for computing the confidence interval of the AUC. Also, using the bootstrap framework, we can conduct a bootstrap test for assessing significance of the difference of AUCs for two diagnostic tests. In addition, we provide an influence diagnostic method based on the AUC by leave-one-study-out analyses. We present illustrative examples using two DTA met-analyses for diagnostic tests of cervical cancer and asthma. We also developed an easy-to-handle R package dmetatools for these computations. The various quantitative evidence provided by these methods certainly supports the interpretations and precise evaluations of statistical evidence of DTA meta-analyses.

Clinically Significant Prostate Cancer Detection With Biparametric MRI: A Systematic Review and Meta-Analysis.

OBJECTIVE. The purpose of this study was to perform a systematic review and a meta-analysis of diagnostic accuracy studies that used biparametric MRI (bpMRI) for the detection of clinically significant prostate cancer (csPCa). MATERIALS AND METHODS. Multiple medical databases were systematically searched to identify articles using bpMRI for csPCa detection. Sensitivity, specificity, PPV, and NPV were calculated for each study after enough data were extracted to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Meta-analyses based on bivariate random-effects methods were used to calculate pooled sensitivity, specificity, and summary ROC (SROC) curves. A meta-regression analysis was performed to assess heterogeneity sources. RESULTS. A total of 17 studies (3964 patients) that adopted PI-RADS or other scoring systems were included. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio of bpMRI in the detection of csPCa were 0.83 (95% CI, 0.76-0.88), 0.71 (95% CI, 0.63-0.79), 2.9 (95% CI, 2.3-3.7), 0.24 (95% CI, 0.17-0.33), and 12 (95% CI, 8-19), respectively, with an area under the SROC curve of 0.84 (95% CI, 0.81-0.87). The overall quality of the included studies was heterogeneous. CONCLUSION. Our results confirm the feasibility of bpMRI for the detection of csPCa and for reducing acquisition time, patient discomfort, and costs. Nevertheless, the available studies proved to be heterogeneous, indicating a need for a more robust validation of this imaging protocol and a standardization of prostate bpMRI acquisition and reporting.

A new method for synthesizing test accuracy data outperformed the bivariate method

ObjectivesThis study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model. MethodsThe SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity. ResultsThe SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method. ConclusionThe SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the diagma module in Stata and the SCSmeta function in R.

The 100 top-cited meta-analyses of diagnostic accuracy in radiology journals: a bibliometric analysis

PurposeTo identify the 100 top-cited meta-analyses of diagnostic accuracy studies published in radiology, medical imaging and nuclear medicine journals.MethodsA PubMed search with pre-defined criteria was performed. The 100 top-cited articles meta-analyses were retrieved, using a custom Python script and the Scopus Application Programming Interface (Elsevier). Publication, citation and affiliation details were extracted from each meta-analysis. No formal statistical analysis was performed.ResultsThe top meta-analysis was cited 394 times, the 100th meta-analysis 38 times. The USA was the top country represented in the papers (33 meta-analyses) followed by The Netherlands, China and Germany. The journal Radiology published 24 studies. The most common modality reported was positron emission tomography (PET) or PET computed tomography (36 instances), followed by magnetic resonance imaging (30 instances) and computed tomography (27 instances). Cardiac (19 meta-analyses), abdominal (18 meta-analyses), followed by neurological (12 meta-analyses) investigations were the most frequently encountered in the top 100 cited meta-analyses.ConclusionsThe 100 top-cited meta-analyses encompass a broad range of imaging modalities and body regions. This may comprise a useful resource for identifying influential evidence-based diagnostic accuracy information in radiology.

The Diagnostic Accuracy of Magnetic Resonance Imaging in Restaging of Rectal Cancer After Preoperative Chemoradiotherapy: A Meta-Analysis and Systematic Review.

To evaluate the overall diagnostic value of magnetic resonance imaging (MRI) in restaging of rectal cancer after preoperative chemoradiotherapy based on qualified studies. PubMed, Cochrane, and EMBASE database were searched by the index words to identify the qualified studies, and relevant literature sources were also searched. The latest research was done in April 2019. Heterogeneity of the included studies was tested, which was used to select proper effect model to calculate pooled weighted sensitivity, specificity, and diagnostic odds ratio (DOR). Summary receiver operating characteristic (SROC) analyses were also performed. Nineteen studies with 1262 patients were involved in the meta-analysis exploring the diagnostic accuracy of MRI for rectal cancer. The diagnostic accuracy of MRI in T3-T4 rectal cancer was as follows: sensitivity, 81% (95% confidence interval [CI], 67%-90%); specificity, 67% (95% CI, 51%-80%); positive likelihood ratio, 2.48 (95% CI, 1.57-3.91); negative likelihood ratio, 0.28 (95% CI, 0.15-0.52); global DOR, 6.86 (95% CI, 3.07-15.30); the area under the SROC was high (0.81; 95% CI, 0.78-0.84). The diagnostic accuracy of MRI in lymphatic metastasis of rectal cancer was as follows: sensitivity, 77% (95% CI, 65%-86%); specificity, 77% (95% CI, 63%-87%); positive likelihood ratio, 3.40 (95% CI, 2.07-5.59); negative likelihood ratio, 0.30 (95% CI, 0.20-0.45); DOR, 10.81 (95% CI, 4.99-23.39); area under the SROC was high (0.84; 95% CI, 0.80-0.87). This study provides a systematic review and meta-analysis of diagnostic accuracy studies of MRI for rectal cancer. The results indicate that MRI is a highly accurate diagnostic tool for rectal cancer T3-T4 staging and N staging but sensitivity and specificity are not high.