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

Imaging in assessing hepatic and peritoneal metastases of gastric cancer: a systematic review

<p>Abstract</p> <p>Background</p> <p>Hepatic and peritoneal metastases of gastric cancer are operation contraindications. Systematic review to provide an overview of imaging in predicting the status of liver and peritoneum pre-therapeuticly is essential.</p> <p>Methods</p> <p>A systematic review of relevant literatures was performed in Pubmed/Medline, Embase, The Cochrane Library and the China Biological Medicine Databases. QUADAS was used for assessing the methodological quality of included studies and the bivariate model was used for this meta-analysis.</p> <p>Results</p> <p>Totally 33 studies were included (8 US studies, 5 EUS studies, 22 CT studies, 2 MRI studies and 5 18F-FDG PET studies) and the methodological quality of included studies was moderate. The result of meta-analysis showed that CT is the most sensitive imaging method [0.74 (95% CI: 0.59-0.85)] with a high rate of specificity [0.99 (95% CI: 0.97-1.00)] in detecting hepatic metastasis, and EUS is the most sensitive imaging modality [0.34 (95% CI: 0.10-0.69) ] with a specificity of 0.96 (95% CI: 0.87-0.99) in detecting peritoneal metastasis. Only two eligible MRI studies were identified and the data were not combined. The two studies found that MRI had both high sensitivity and specificity in detecting liver metastasis.</p> <p>Conclusion</p> <p>US, EUS, CT and ¹⁸F-FDG PET did not obtain consistently high sensitivity and specificity in assessing liver and peritoneal metastases of gastric cancer. The value of laparoscopy, PET/CT, DW-MRI, and new PET tracers such as ¹⁸F-FLT needs to be studied in future.</p

Chapter 8: Meta-analysis of Test Performance When There is a “Gold Standard”

Synthesizing information on test performance metrics such as sensitivity, specificity, predictive values and likelihood ratios is often an important part of a systematic review of a medical test. Because many metrics of test performance are of interest, the meta-analysis of medical tests is more complex than the meta-analysis of interventions or associations. Sometimes, a helpful way to summarize medical test studies is to provide a “summary point”, a summary sensitivity and a summary specificity. Other times, when the sensitivity or specificity estimates vary widely or when the test threshold varies, it is more helpful to synthesize data using a “summary line” that describes how the average sensitivity changes with the average specificity. Choosing the most helpful summary is subjective, and in some cases both summaries provide meaningful and complementary information. Because sensitivity and specificity are not independent across studies, the meta-analysis of medical tests is fundamentaly a multivariate problem, and should be addressed with multivariate methods. More complex analyses are needed if studies report results at multiple thresholds for positive tests. At the same time, quantitative analyses are used to explore and explain any observed dissimilarity (heterogeneity) in the results of the examined studies. This can be performed in the context of proper (multivariate) meta-regressions

Diagnostic value of fine-needle aspiration biopsy for breast mass: a systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Fine-needle aspiration biopsy (FNAB) of the breast is a minimally invasive yet maximally diagnostic method. However, the clinical use of FNAB has been questioned. The purpose of our study was to establish the overall value of FNAC in the diagnosis of breast lesions.</p> <p>Methods</p> <p>After a review and quality assessment of 46 studies, sensitivity, specificity and other measures of accuracy of FNAB for evaluating breast lesions were pooled using random-effects models. Summary receiver operating characteristic curves were used to summarize overall accuracy. The sensitivity and specificity for the studies data (included unsatisfactory samples) and underestimation rate of unsatisfactory samples were also calculated.</p> <p>Results</p> <p>The summary estimates for FNAB in diagnosis of breast carcinoma were as follows (unsatisfactory samples was temporarily exluded): sensitivity, 0.927 (95% confidence interval [CI], 0.921 to 0.933); specificity, 0.948 (95% CI, 0.943 to 0.952); positive likelihood ratio, 25.72 (95% CI, 17.35 to 28.13); negative likelihood ratio, 0.08 (95% CI, 0.06 to 0.11); diagnostic odds ratio, 429.73 (95% CI, 241.75 to 763.87); The pooled sensitivity and specificity for 11 studies, which reported unsatisfactory samples (unsatisfactory samples was considered to be positive in this classification) were 0.920 (95% CI, 0.906 to 0.933) and 0.768 (95% CI, 0.751 to 0.784) respectively. The pooled proportion of unsatisfactory samples that were subsequently upgraded to various grade cancers was 27.5% (95% CI, 0.221 to 0.296).</p> <p>Conclusions</p> <p>FNAB is an accurate biopsy for evaluating breast malignancy if rigorous criteria are used. With regard to unsatisfactory samples, futher invasive procedures are required in order to minimize the chance of a missed diagnosis of breast cancer.</p

O6-Methylguanine-DNA methyltransferase protein expression by immunohistochemistry in brain and non-brain systemic tumours: systematic review and meta-analysis of correlation with methylation-specific polymerase chain reaction

Background: The DNA repair protein O6-Methylguanine-DNA methyltransferase (MGMT) confers resistance to alkylating agents. Several methods have been applied to its analysis, with methylation-specific polymerase chain reaction (MSP) the most commonly used for promoter methylation study, while immunohistochemistry (IHC) has become the most frequently used for the detection of MGMT protein expression. Agreement on the best and most reliable technique for evaluating MGMT status remains unsettled. The aim of this study was to perform a systematic review and meta-analysis of the correlation between IHC and MSP. Methods A computer-aided search of MEDLINE (1950-October 2009), EBSCO (1966-October 2009) and EMBASE (1974-October 2009) was performed for relevant publications. Studies meeting inclusion criteria were those comparing MGMT protein expression by IHC with MGMT promoter methylation by MSP in the same cohort of patients. Methodological quality was assessed by using the QUADAS and STARD instruments. Previously published guidelines were followed for meta-analysis performance. Results Of 254 studies identified as eligible for full-text review, 52 (20.5%) met the inclusion criteria. The review showed that results of MGMT protein expression by IHC are not in close agreement with those obtained with MSP. Moreover, type of tumour (primary brain tumour vs others) was an independent covariate of accuracy estimates in the meta-regression analysis beyond the cut-off value. Conclusions Protein expression assessed by IHC alone fails to reflect the promoter methylation status of MGMT. Thus, in attempts at clinical diagnosis the two methods seem to select different groups of patients and should not be used interchangeably