Alexey Surov1, Hans Jonas Meyer2, Andreas Wienke3. 1. Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany Alexey.Surov@medizin.uni-leipzig.de. 2. Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany. 3. Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany.
Abstract
BACKGROUND/AIM: Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion that can provide information about tissue microstructure, especially about cell count. Increase of cell density induces restriction of water diffusion and decreases apparent diffusion coefficient (ADC). ADC can be divided into three sub-parameters: ADC minimum or ADCmin, mean ADC or ADCmean and ADC maximum or ADCmax Some studies have suggested that ADCmin shows stronger correlations with cell count in comparison to other ADC fractions and may be used as a parameter for estimation of tumor cellularity. The aim of the present meta-analysis was to summarize correlation coefficients between ADCmin and cellularity in different tumors based on large patient data. PATIENTS AND METHODS: For this analysis, MEDLINE database was screened for associations between ADC and cell count in different tumors up to September 2016. For this work, only data regarding ADCmin were included. Overall, 12 publications with 317 patients were identified. Spearman's correlation coefficient was used to analyze associations between ADCmin and cellularity. The reported Pearson correlation coefficients in some publications were converted into Spearman correlation coefficients. RESULTS: The pooled correlation coefficient for all included studies was ρ=-0.59 (95% confidence interval (CI)=-0.72 to -0.45), heterogeneity Tau2=0.04 (p<0.0001), I2=73%, test for overall effect Z=8.67 (p<0.00001). CONCLUSION: ADCmin correlated moderately with tumor cellularity. The calculated correlation coefficient is not stronger in comparison to the reported coefficient for ADCmean and, therefore, ADCmin does not represent a better means to reflect cellularity. Copyright
BACKGROUND/AIM: Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion that can provide information about tissue microstructure, especially about cell count. Increase of cell density induces restriction of water diffusion and decreases apparent diffusion coefficient (ADC). ADC can be divided into three sub-parameters: ADC minimum or ADCmin, mean ADC or ADCmean and ADC maximum or ADCmax Some studies have suggested that ADCmin shows stronger correlations with cell count in comparison to other ADC fractions and may be used as a parameter for estimation of tumor cellularity. The aim of the present meta-analysis was to summarize correlation coefficients between ADCmin and cellularity in different tumors based on large patient data. PATIENTS AND METHODS: For this analysis, MEDLINE database was screened for associations between ADC and cell count in different tumors up to September 2016. For this work, only data regarding ADCmin were included. Overall, 12 publications with 317 patients were identified. Spearman's correlation coefficient was used to analyze associations between ADCmin and cellularity. The reported Pearson correlation coefficients in some publications were converted into Spearman correlation coefficients. RESULTS: The pooled correlation coefficient for all included studies was ρ=-0.59 (95% confidence interval (CI)=-0.72 to -0.45), heterogeneity Tau2=0.04 (p<0.0001), I2=73%, test for overall effect Z=8.67 (p<0.00001). CONCLUSION: ADCmin correlated moderately with tumor cellularity. The calculated correlation coefficient is not stronger in comparison to the reported coefficient for ADCmean and, therefore, ADCmin does not represent a better means to reflect cellularity. Copyright
Authors: Ralf S Eschbach; Philipp M Kazmierczak; Maurice M Heimer; Andrei Todica; Heidrun Hirner-Eppeneder; Moritz J Schneider; Georg Keinrath; Olga Solyanik; Jessica Olivier; Wolfgang G Kunz; Maximilian F Reiser; Peter Bartenstein; Jens Ricke; Clemens C Cyran Journal: Cancer Imaging Date: 2018-01-18 Impact factor: 3.909