The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.

The objective of this paper is to describe general approaches of diagnostic test accuracy (DTA) that are available for the quantitative synthesis of data using R software. We conduct a DTA that summarizes statistics for univariate analysis and bivariate analysis. The package commands of R software were “metaprop” and “metabin” for sensitivity, specificity, and diagnostic odds ratio; forest for forest plot; reitsma of “mada” for a summarized receiver-operating characteristic (ROC) curve; and “metareg” for meta-regression analysis. The estimated total effect sizes, test for heterogeneity and moderator effect, and a summarized ROC curve are reported using R software. In particular, we focus on how to calculate the effect sizes of target studies in DTA. This study focuses on the practical methods of DTA rather than theoretical concepts for researchers whose fields of study were non-statistics related. By performing this study, we hope that many researchers will use R software to determine the DTA more easily, and that there will be greater interest in related research.

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.

This was a study to compare the diagnostic efficacies of endoscopic ultrasonography (EUS), CT, PET/MR imaging, and PET/CT for the preoperative local and regional staging of esophageal cancer, with postoperative pathologic stage used as the reference standard. During 1 y, 19 patients with resectable esophageal cancer were enrolled and underwent preoperative EUS, CT, PET/CT, and PET/MR imaging. A chest radiologist and nuclear medicine physician retrospectively reviewed the images and assigned tumor and lymph node stages according to the seventh version of the TNM system and the American Joint Committee on Cancer staging system. Four patients who were treated nonsurgically were excluded from data analysis. The efficacies of EUS, CT, PET/CT, and PET/MR imaging were compared. Primary tumors were correctly staged in 13 (86.7%), 10 (66.7%), and 5 (33.3%) patients at EUS, PET/MR imaging, and CT, respectively (P value ranging from 0.021 to 0.375). The accuracy of determining T1 lesions was 86.7%, 80.0%, and 46.7% for EUS, PET/MR imaging, and CT, respectively. For distinguishing T3 lesions, the accuracy was 93.3% for EUS and 86.7% for both PET/MR imaging and CT. For lymph node staging, the accuracy was 83.3%, 75.0%, 66.7%, and 50.0% for PET/MR imaging, EUS, PET/CT, and CT, respectively. In addition, area-under-the-curve values were 0.800, 0.700, 0.629, and 0.543 for PET/MR imaging, EUS, PET/CT, and CT, respectively. PET/MR imaging demonstrated acceptable accuracy for T staging compared with EUS and, although not statistically significant, even higher accuracy than EUS and PET/CT for prediction of N staging. With adjustments in protocols, PET/MR imaging may provide an important role in preoperative esophageal cancer staging in the future.

Background: The aim of this prospective study was to evaluate the predictive value of [¹⁸F]fluorodeoxyglucose positron emission tomography (FDG PET) for the pathological response of breast cancer after completion of neo-adjuvant chemotherapy. Methods: Fifty patients with newly diagnosed, non-inflammatory, large or locally advanced breast cancer undergoing neo-adjuvant chemotherapy were eligible for this study. Clinical assessment was accomplished by comparing initial tumor size with preoperative tumor size. Pathological responses were classified into three groups: pathological non-response (pNR), pathological partial response (pPR) and pathological complete response (pCR). To determine the effect of reduction rate (RR) of peak standardized uptake values for tumor responses, logistic regression analyses were performed. To identify an optimal threshold value of RR for the prediction of pathological response, receiver operating characteristic analysis was performed. Results: Eight per cent (four of 50) of the patients had pCR and 46% had pPR. Ten per cent of patients had clinical CR and 52% had clinical PR. In clinical response, the RRs (±SD) of CR (−83.4±12), PR (−81.8±22.7) and NR (−79.7±31.9) showed no statistical differences (P >0.05). However, for pathological responses, the RR of CR (−96.5±3.4) had a lower value than those of PR (−87.9±15.1) and NR (−56.2±29.6) (P=0.0006; CR versus PR, P <0.05; CR versus NR, P <0.05; PR versus NR, P <0.01). When −88% of RR was used as threshold value for differentiation between pCR and pPR, the area under the curve (AUC) was 0.788 [standard error (SE) 0.106; 95% confidence interval (CI) 0.589–0.920]. The sensitivity and specificity were 100% and 56.5%, respectively. When −79% of RR was used as threshold value for differentiation between pathological responders and non-responders, the AUC was 0.838 (SE 0.059; 95% CI 0.707–0.927). The sensitivity and specificity were 85.2% and 82.6%, respectively. Conclusions: Despite some limitations, this study suggests a possible predictive value of FDG PET for the assessment of the pathological response of primary breast cancer after neo-adjuvant chemotherapy. However, these findings deserve further investigation on a larger number of patients, and more frequent and earlier PET scans in each patient need to be performed to allow a better validation of the differentiation between the responder and non-responder groups.