Two networks generated by the CC model.

<p>The functions of the CC model are set to be <i>N</i>(<i>t</i>) = 5, </p><p></p><p><mo stretchy="false">∣</mo></p><p><mi>D</mi><mi>i</mi></p><mo stretchy="false">∣</mo><mo>=</mo><p></p><p><mn>0</mn><mo>.</mo><mn>2</mn><mi>β</mi><mo stretchy="false">(</mo></p><p><mi>θ</mi><mi>i</mi></p><mo stretchy="false">)</mo><p></p><p><mi>t</mi><mi>i</mi></p><p></p><p></p><p></p> for the case in Panel(a), and <i>N</i>(<i>t</i>) = [e^0.1<i>t</i>], <p></p><p><mo stretchy="false">∣</mo></p><p><mi>D</mi><mi>i</mi></p><mo stretchy="false">∣</mo><mo>=</mo><p></p><p><mn>0</mn><mo>.</mo><mn>15</mn><mi>β</mi><mo stretchy="false">(</mo></p><p><mi>θ</mi><mi>i</mi></p><mo stretchy="false">)</mo><p></p><p><mo stretchy="false">[</mo></p><p>e</p><p><mn>0</mn><mo>.</mo><mn>1</mn></p><p><mi>t</mi><mi>i</mi></p><p></p><p></p><mo stretchy="false">]</mo><p></p><p></p><p></p><p></p> for the case in Panel(b). <i>β</i>(⋅) is given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120687#pone.0120687.e005" target="_blank">Equation (2)</a> for both cases.<p></p

Out-degree distributions of the citation networks in Table 1 and the fitting curves of the distributions.

<p>The fitting model is the mixture generalized Poisson distribution (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120687#pone.0120687.e032" target="_blank">Equation (14)</a>).</p

The goodness for fitting the in-degree distributions of some citation networks by the power-law function <i>f</i>(<i>k</i>) = <i>ak</i>⁻².

<p>The goodness for fitting the in-degree distributions of some citation networks by the power-law function <i>f</i>(<i>k</i>) = <i>ak</i>⁻².</p

Empirical citation networks.

<p>Empirical citation networks.</p

The in- and out-degree distributions of a network generated by the CC model.

<p>The functions of the CC model are set as follows: <i>N</i>(<i>t</i>) = [e^0.1<i>t</i>], </p><p></p><p><mo stretchy="false">∣</mo></p><p><mi>D</mi><mi>i</mi></p><mo stretchy="false">∣</mo><mo>=</mo><p></p><p><mn>0</mn><mo>.</mo><mn>15</mn><mi>β</mi><mo stretchy="false">(</mo></p><p><mi>θ</mi><mi>i</mi></p><mo stretchy="false">)</mo><p></p><p><mo stretchy="false">[</mo></p><p>e</p><p><mn>0</mn><mo>.</mo><mn>1</mn></p><p><mi>t</mi><mi>i</mi></p><p></p><p></p><mo stretchy="false">]</mo><p></p><p></p><p></p><p></p>, and <i>β</i>(⋅) is given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120687#pone.0120687.e005" target="_blank">Equation (2)</a>. The fitting functions in Panel (a) are the Poisson distribution <p></p><p><mi>f</mi><mo stretchy="false">(</mo><mi>k</mi><mo stretchy="false">)</mo><mo>=</mo></p><p><mi>a</mi><mi>k</mi></p><p></p><p>e</p><p><mo>−</mo><mi>a</mi></p><p></p><p><mi>k</mi><mo>!</mo></p><p></p><p></p><p></p> and the mixture Poisson distribution given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120687#pone.0120687.e031" target="_blank">Equation (13)</a>. The fitting functions in Panel (b) are the power-law functions <i>f</i>(<i>k</i>) = <i>ak</i>⁻² and <p></p><p><mi>f</mi><mo stretchy="false">(</mo><mi>k</mi><mo stretchy="false">)</mo><mo>=</mo></p><p></p><p></p><p><mi>k</mi></p><p><mo>−</mo><mi>γ</mi></p><p></p><p></p><p></p><p></p><p><mo>∑</mo></p><p><mi>n</mi><mo>=</mo><mn>0</mn></p><mi>∞</mi><p></p><p></p><p></p><p><mo stretchy="false">(</mo><mi>n</mi><mo>+</mo></p><p><mi>x</mi></p><p><mi>min</mi></p><p></p><mo stretchy="false">)</mo><p></p><p><mo>−</mo><mi>γ</mi></p><p></p><p></p><p></p><p></p><p></p><p></p><p></p>.<p></p

Diagnostic Accuracy of 2D-Shear Wave Elastography for Liver Fibrosis Severity: A Meta-Analysis

<div><p>Purpose</p><p>To evaluate the accuracy of shear wave elastography (SWE) in the quantitative diagnosis of liver fibrosis severity.</p><p>Methods</p><p>The published literatures were systematically retrieved from PubMed, Embase, Web of science and Scopus up to May 13^th, 2016. Included studies reported the pooled sensitivity, specificity, positive and negative predictive values, as well as the diagnostic odds ratio of SWE in populations with liver fibrosis. A bivariate mixed-effects regression model was used, which was estimated by the <i>I</i>^<i>2</i> statistics. The quality of articles was evaluated by quality assessment of diagnostic accuracy studies (QUADAS).</p><p>Results</p><p>Thirteen articles including 2303 patients were qualified for the study. The pooled sensitivity and specificity of SWE for the diagnosis of liver fibrosis are as follows: <b>≥</b>F1 0.76 (<i>p</i><0.001, 95% CI, 0.71–0.81, <i>I</i>^<i>2</i> = 75.33%), 0.92 (<i>p</i><0.001, 95% CI, 0.80–0.97, <i>I</i>^<i>2</i> = 79.36%); <b>≥</b>F2 0.84 (<i>p</i> = 0.35, 95% CI, 0.81–0.86, <i>I</i>^<i>2</i> = 9.55%), 0.83 (<i>p</i><0.001, 95% CI, 0.77–0.88, <i>I</i>^<i>2</i> = 86.56%); <b>≥</b>F3 0.89 (<i>p</i> = 0.56, 95% CI, 0.86–0.92, <i>I</i>^<i>2</i> = 0%), 0.86 (<i>p</i><0.001, 95% CI, 0.82–0.90, <i>I</i>^<i>2</i> = 75.73%); F4 0.89 (<i>p</i> = 0.24, 95% CI, 0.84–0.92, <i>I</i>^<i>2</i> = 20.56%), 0.88 (<i>p</i><0.001, 95% CI, 0.84–0.92, <i>I</i>^<i>2</i> = 82.75%), respectively. Sensitivity analysis showed no significant changes if any one of the studies was excluded. Publication bias was not detected in this meta-analysis.</p><p>Conclusions</p><p>Our study suggests that SWE is a helpful method to appraise liver fibrosis severity. Future studies that validate these findings would be appropriate.</p></div

Classification performance of the single metrics and multi-modal combinations.

<p>Classification performance of the single metrics and multi-modal combinations.</p

Quality assessment for included studies by QUADAS.

<p>Quality assessment for included studies by QUADAS.</p

The number of features retained in the multi-model method per fold.

<p>The number of features retained in the multi-model method per fold.</p

A flowchart of the multi-model method for classification.

<p>A flowchart of the multi-model method for classification.</p