Bivariate random effects and hierarchical meta-analysis of summary receiver operating characteristic curve on fine needle aspiration cytology

Erte, İdil
In this study, meta-analysis of diagnostic tests, Summary Receiver Operating Characteristic (SROC) curve, bivariate random effects and Hierarchical Summary Receiver Operating Characteristic (HSROC) curve theories have been discussed and accuracy in literature of Fine Needle Aspiration (FNA) biopsy that is used in the diagnosis of masses in breast cancer (malignant or benign) has been analyzed. FNA Cytological (FNAC) examination in breast tumor is, easy, effective, effortless, and does not require special training for clinicians. Because of the uncertainty related to FNAC‘s accurate usage in publications, 25 FNAC studies have been gathered in the meta-analysis. In the plotting of the summary ROC curve, the logit difference and sums of the true positive rates and the false positive rates included in the meta-analysis‘s codes have been generated by SAS. The formula of the bivariate random effects model and hierarchical summary ROC curve is presented in context with the literature. Then bivariate random effects implementation with the new SAS PROC GLIMMIX is generated. Moreover, HSROC implementation is generated by SAS PROC HSROC NLMIXED. Curves are plotted with RevMan Version 5 (2008). It has been stated that the meta-analytic results of bivariate random effects are nearly identical to the results from the HSROC approach. The results achieved through both random effects meta-analytic methods prove that FNA Cytology is a diagnostic test with a high level of distinguish over breast tumor.


CAD for detection of microcalcification and classification in mammograms
AKBAY, Cansu; Gençer, Nevzat Güneri; GENÇER, Gülay (2014-10-17)
In this study, computer aided diagnosis (CAD) is developed to detect microcalficication cluster which is one of the important radiological findings of breast cancer diagnosis and classificiation. For this purpose, image processing and pattern recognition algorithms are applied on mamographic images. To make microcalcifications more visible wavelet transform and nonsubsampled contourlet transform (NSCT) methods are used for image enhancement. Their performances are compared. 52 features are extracted from th...
Capillary electrophoresis with online stacking in combination with AgNPs@MCM-41 reinforced hollow fiber solid-liquid phase microextraction for quantitative analysis of Capecitabine and its main metabolite 5-Fluorouracil in plasma samples isolated from cancer patients
Forough, Mehrdad; Farhadi, Khalil; Molaei, Rahim; Khalili, Hedayat; Shakeri, Ramin; Zamani, Asghar; Matin, Amir Abbas (2017-01-01)
The purpose of this study is the development and validation of a simple, novel, selective and fast off-line microextraction technique combining capillary electrophoresis with in-column field-amplified sample injection (FASI) for the simultaneous determination of capecitabine (CAP) and its active metabolite, 5-Fluorouracil (5-FU), in human plasma. At the moment, there is a lack of using cost-effective CE tool combined with novel miniaturized sample clean-up techniques for analysis of these important anticanc...
Analysis of factors affecting baseline SF-36 Mental Component Summary in Adult Spinal Deformity and its impact on surgical outcomes
Mmopelwa, Tiro; Ayhan, Selim; Yuksel, Selcen; Nabiyev, Vugar; Niyazi, Asli; Pellise, Ferran; Alanay, Ahmet; Sanchez Perez Grueso, Francisco Javier; Kleinstuck, Frank; Obeid, Ibrahim; Acaroglu, Emre (AVES Publishing Co., 2018-5)
Objectives: To identify the factors that affect SF-36 mental component summary (MCS) in patients with adult spinal deformity (ASD) at the time of presentation, and to analyse the effect of SF-36 MCS on clinical outcomes in surgically treated patients. Methods: Prospectively collected data from a multicentric ASD database was analysed for baseline parameters. Then, the same database for surgically treated patients with a minimum of 1-year follow-up was analysed to see the effect of baseline SF-36 MCS on t...
Modeling diseases with multiple disease characteristics: comparison of models and estimation methods
Erdem, Münire Tuğba; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2011)
Epidemiological data with disease characteristic information can be modelled in several ways. One way is taking each disease characteristic as a response and constructing binary or polytomous logistic regression model. Second way is using a new response which consists of disease subtypes created by cross-classification of disease characteristic levels, and then constructing polytomous logistic regression model. The former may be disadvantageous since any possible covariation between disease characteristics ...
Turn to Turn Fault Diagnosis for Induction Machines Based on Wavelet Transformation and BP Neural Network
Najafi, Atabak; Iskender, Iris; Farhadi, Pavam; Najafi, Babak (2011-09-10)
Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn...
Citation Formats
İ. Erte, “Bivariate random effects and hierarchical meta-analysis of summary receiver operating characteristic curve on fine needle aspiration cytology,” M.S. - Master of Science, Middle East Technical University, 2011.