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      Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADC mean

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      Oncotarget
      Impact Journals LLC
      diffusion weighted imaging, ADC, ki 67

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          Abstract

          Diffusion weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique based on measure of water diffusion in tissues. This diffusion can be quantified by apparent diffusion coefficient (ADC). Some reports indicated that ADC can reflect tumor proliferation potential. The purpose of this meta-analysis was to provide evident data regarding associations between ADC and KI 67 in different tumors. Studies investigating the relationship between ADC and KI 67 in different tumors were identified.

          MEDLINE library was screened for associations between ADC and KI 67 in different tumors up to April 2017. Overall, 42 studies with 2026 patients were identified. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.

          Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients.

          The pooled correlation coefficient between ADC mean and KI 67 for all included tumors was ρ = −0.44. Furthermore, correlation coefficient for every tumor entity was calculated. The calculated correlation coefficients were as follows: ovarian cancer: ρ = −0.62, urothelial carcinomas: ρ = −0.56, cerebral lymphoma: ρ = −0.55, neuroendocrine tumors: ρ = −0.52, glioma: ρ = −0.51, lung cancer: ρ = −0.50, prostatic cancer: ρ = −0.43, rectal cancer: ρ = −0.42, pituitary adenoma: ρ = −0.44, meningioma, ρ = −0.43, hepatocellular carcinoma: ρ = −0.37, breast cancer: ρ = −0.22.

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          Most cited references58

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          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 .
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            Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations.

            On May 3, 2008, a National Cancer Institute (NCI)-sponsored open consensus conference was held in Toronto, Ontario, Canada, during the 2008 International Society for Magnetic Resonance in Medicine Meeting. Approximately 100 experts and stakeholders summarized the current understanding of diffusion-weighted magnetic resonance imaging (DW-MRI) and reached consensus on the use of DW-MRI as a cancer imaging biomarker. DW-MRI should be tested as an imaging biomarker in the context of well-defined clinical trials, by adding DW-MRI to existing NCI-sponsored trials, particularly those with tissue sampling or survival indicators. Where possible, DW-MRI measurements should be compared with histologic indices including cellularity and tissue response. There is a need for tissue equivalent diffusivity phantoms; meanwhile, simple fluid-filled phantoms should be used. Monoexponential assessments of apparent diffusion coefficient values should use two b values (>100 and between 500 and 1000 mm2/sec depending on the application). Free breathing with multiple acquisitions is superior to complex gating techniques. Baseline patient reproducibility studies should be part of study designs. Both region of interest and histogram analysis of apparent diffusion coefficient measurements should be obtained. Standards for measurement, analysis, and display are needed. Annotated data from validation studies (along with outcome measures) should be made publicly available. Magnetic resonance imaging vendors should be engaged in this process. The NCI should establish a task force of experts (physicists, radiologists, and oncologists) to plan, organize technical aspects, and conduct pilot trials. The American College of Radiology Imaging Network infrastructure may be suitable for these purposes. There is an extraordinary opportunity for DW-MRI to evolve into a clinically valuable imaging tool, potentially important for drug development.
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              The size of the nucleus increases as yeast cells grow.

              It is not known how the volume of the cell nucleus is set, nor how the ratio of nuclear volume to cell volume (N/C) is determined. Here, we have measured the size of the nucleus in growing cells of the budding yeast Saccharomyces cerevisiae. Analysis of mutant yeast strains spanning a range of cell sizes revealed that the ratio of average nuclear volume to average cell volume was quite consistent, with nuclear volume being approximately 7% that of cell volume. At the single cell level, nuclear and cell size were strongly correlated in growing wild-type cells, as determined by three different microscopic approaches. Even in G1-phase, nuclear volume grew, although it did not grow quite as fast as overall cell volume. DNA content did not appear to have any immediate, direct influence on nuclear size, in that nuclear size did not increase sharply during S-phase. The maintenance of nuclear size did not require continuous growth or ribosome biogenesis, as starvation and rapamycin treatment had little immediate impact on nuclear size. Blocking the nuclear export of new ribosomal subunits, among other proteins and RNAs, with leptomycin B also had no obvious effect on nuclear size. Nuclear expansion must now be factored into conceptual and mathematical models of budding yeast growth and division. These results raise questions as to the unknown force(s) that expand the nucleus as yeast cells grow.

                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                26 September 2017
                24 August 2017
                : 8
                : 43
                : 75434-75444
                Affiliations
                1 Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
                2 Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin Luther University of Halle-Wittenberg, Halle, Germany
                Author notes
                Correspondence to: Alexey Surov, Alexey.Surov@ 123456medizin.uni-leipzig.de
                [*]

                These authors contributed equally to this work

                Article
                20406
                10.18632/oncotarget.20406
                5650434
                29088879
                6f03fcc3-472f-444e-b25d-84e6b77447bd
                Copyright: © 2017 Surov et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 June 2017
                : 15 August 2017
                Categories
                Meta-Analysis

                Oncology & Radiotherapy
                diffusion weighted imaging,adc,ki 67
                Oncology & Radiotherapy
                diffusion weighted imaging, adc, ki 67

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