Endometrial Thickness Measurement for Detecting Endometrial Cancer in Women With Postmenopausal Bleeding
Anne TimmermansBrent C. OpmeerKhalid S. KhanLucas M. BachmannE. EpsteinT. Justin ClarkJanesh GuptaShagaf BakourT. Van den BoschHelena C. van DoornSharon CameronMaria GiusaStefano DessoleF. P. H. L. J. DijkhuizenGerben ter RietBen W. Mol
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To estimate the accuracy of endometrial thickness measurement in the detection of endometrial cancer among women with postmenopausal bleeding with individual patient data using different meta-analytic strategies.Original data sets of studies detected after reviewing the included studies of three previous reviews on this subject. An additional literature search of published articles using MEDLINE databases was preformed from January 2000 to December 2006 to identify articles reporting on endometrial carcinoma and sonographic endometrial thickness measurement in women with postmenopausal bleeding.We identified 90 studies reporting on endometrial thickness measurements and endometrial carcinoma in women with postmenopausal bleeding.We contacted 79 primary investigators to obtain the individual patient data of their reported studies, of which 13 could provide data. Data on 2,896 patients, of which 259 had carcinoma, were included. Several approaches were used in the analyses of the acquired data. First, we performed receiver operator characteristics (ROC) analysis per study, resulting in a summary area under the ROC curve (AUC) calculated as a weighted mean of AUCs from original studies. Second, individual patient data were pooled and analyzed with ROC analyses irrespective of study with standardization of distributional differences across studies using multiples of the median and by random effects logistic regression. Finally, we also used a two-stage procedure, calculating sensitivities and specificities for each study and using the bivariate random effects model to estimate summary estimates for diagnostic accuracy. This resulted in rather comparable ROC curves with AUCs varying between 0.82 and 0.84 and summary estimates for sensitivity and specificity located along these curves. These curves indicated a lower AUC than previously reported meta-analyses using conventional techniques.Previous meta-analyses on endometrial thickness measurement probably have overestimated its diagnostic accuracy in the detection of endometrial carcinoma. We advise the use of cutoff level of 3 mm for exclusion of endometrial carcinoma in women with postmenopausal bleeding.Keywords:
POSTMENOPAUSAL BLEEDING
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POSTMENOPAUSAL BLEEDING
Uterine bleeding
Vaginal bleeding
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(Abstracted from Acta Obstet Gynecol Scand 2016;95(12):1418–1424) Women with postmenopausal bleeding who present with an endometrial thickness 4 mm or less are at a very low risk of endometrial cancer, and therefore refraining from endometrial sampling in these women is considered justified. If the endometrial thickness is more than 4 mm, endometrial sampling is indicated to exclude endometrial cancer.
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BackgroundMultivariate meta-analysis is used when multiple correlated outcomes are reported in a systematic review. This study explored the application of multivariate meta-analysis in such a context. The objectives of the present study were to compare the summary findings and decisions between univariate and bivariate meta-analyses, as well as to assess how much sensitive the results are towards the strength of the correlation between the outcome variables.MethodsA systematic review that reported two correlated outcomes, Intact parathyroid hormone levels and serum phosphate was chosen for demonstrating the applications of bivariate meta-analysis. Both univariate and bivariate meta-analyses with fixed effect and random effect models were carried out and the results were compared. A sensitivity analysis was performed for a wide spectrum of correlations from −1 to +1 to assess the impact of correlation on pooled effect estimates and its precision.ResultsPooled effect estimates generated through bivariate meta-analysis were found to be varying when compared to those obtained through univariate meta-analysis. The confidence interval of the pooled effect estimates obtained through bivariate meta-analysis was wider than in univariate meta-analysis. Further, the value of the pooled effect estimates along with its confidence intervals also differed for varied levels of correlations.ConclusionsThis study observed that when we have multiple correlated outcome variables to answer a single question bivariate meta-analysis could be a better approach. The magnitude of the correlation between the outcome variables also plays a vital role in meta-analysis.
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To evaluate efficiency of pattern diagnosis of endometrial cancer obtained by use of the risk of endometrial cancer (REC) score by transvaginal sonography (TVS) and gelinfusion sonography (GIS) in women with postmenopausal bleeding (PMB). Consecutive women (950) with PMB had TVS. In 457 women TVS findings were indefinite and GIS were added. Women were evaluated by residencies supervised by trained sonographers. Endometrial pattern was scored according to the (REC-score) system by adding scores for: BMI (30+ = score 1), endometrial thickness(ET) (10-14 = score 1), ET (15+ = score 1), vascularity, but not a single/double dominant vessel (present = score 1), multiple vessels (present = score 1), large vessels (present = score 1), and splashed/densely packed vessels (present = score 1), interrupted endo-myometrial junction (present = score 1), and irregular surface at GIS (present = score 1). A diagnosis of malignancy was made at a REC-score of ≥3 obtained by TVS or ≥4 by GIS. Reference standard was endometrial samples (ET 4-5 mm) and operative hysteroscopy or hysterectomy (ET >5 mm). The REC-score in 950 women (239 endometrial cancer) showed: sensitivity (95%CI): 93 (89–96); specificity: 93 (91–95); Area under the curve (AUC): 93(91–95). Addition of GIS only improve efficiency marginally. In 708 patients referred directly (63 cancers) sensitivity was 92(82–97) specificity 96(94–96) PPV 67(56–76), NPV 99(98–100). Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Abstract Rationale, aims and objectives Meta‐analyses of diagnostic test accuracy are important elements in evidence‐based medicine. However, currently there is no overview of related quantitative findings that were obtained in a large number of real meta‐analyses. This study aimed at providing such empirical summary. Methods From the literature 50 meta‐analyses were randomly selected that had reported their 2 × 2 count data of sensitivity and specificity. Descriptive statistics, assessment of between‐study heterogeneity and bivariate random‐effects meta‐analysis of sensitivity and specificity were performed with a novel B ayesian program code. The bivariate model parameters were also converted to the parameters of the closely related hierarchical summary receiver operating characteristic ( HSROC ) model. Results Among the 50 meta‐analyses, the studies per meta‐analysis ranged from 5 to 45 and the disease prevalence from 2.3 to 71%. Significant between‐study heterogeneity was found in 43 of 50 meta‐analyses, favouring a random‐effects model over a fixed‐effects model. Empirical distributions of sensitivity and specificity, positive and negative likelihood ratios, and other model results are presented in the full text numerically and graphically. Conclusions Studies of diagnostic test accuracy can be well meta‐analysed within a B ayesian framework, and the presented quantitative findings provide an orientation when interpreting the results of the standard bivariate/ HSROC model.
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This manuscript considers discrepancies between the bivariate correlation and several indices of association estimated from regression results. These indices can be estimated from results typically reported in primary studies. In recent years, many researchers conducting meta‐analyses have used these indices in place of, or together with, the bivariate correlation. I illustrate the differences among these indices and the bivariate correlation. I demonstrate the inaccuracy of these indices as replacements for bivariate effects. Thus, I recommend discontinuing the use of these indices and partial effect sizes as replacement for the bivariate correlation. Copyright © 2014 John Wiley & Sons, Ltd.
Bivariate data
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The Fourier Transform (FT) is a widely used analysis tool.However, FT alone is not suited for the analysis of bivariate signals (e.g., stereophonic recordings), as it is not sensitive to the relationship between channels.Different works addressing this problem can be found in the literature; the Bivariate Mixture Space (BMS) is introduced here as an alternative representation to the existing techniques.BMS is still based on the FT and can be thought of as an extension of it, such that the relationship between two signals is considered as additional information in the frequency domain.Despite being simpler than other techniques aimed at representing bivariate signals, this representation is shown to have some desirable characteristics that are absent in traditional representations, which lead to novel ways to perform linear and non-linear decomposition, feature extraction, and data visualization.As a demonstrative application, an Independent Component Analysis algorithm is derived from the BMS, who shows promising results with respect to existing implementations in terms of performance and robustness.
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