Distribution of 1000 bootstrap results.

<p>Here the results for CRC sample 19 is displayed. These were calculated based on the counting by the second observer during the second round of counting. Tukey boxplots were constructed for amounts of regions of interest evaluated. Ten regions are sufficient for accurate microvessel density calculation.</p

Histological heterogeneity of CD31-stained blood vessels in glioblastoma multiforme (a-d) and renal cell carcinoma (e-h).

<p>(a-b) Q_A = 15 vessels per mm², A_A = 1.56%, (c-d) Q_A = 77 vessels per mm², A_A = 3.70%, (e-f) Q_A = 183 vessels per mm², A_A = 13.10%, (g-h) Q_A = 81 vessels per mm², A_A = 6.17%. Low (a, b, e, f) heterogeneous samples showed a uniform distribution of vessel profiles as compared to high (c, d, g, h) heterogeneous samples. In glioblastoma multiforme, hotspots and garlands (arrows) were more easily recognized in heterogeneous than in homogeneous samples. Scale bar represents 500 μm (a, c, e, g) or 100 μm (b, d, f, h)</p

Intra- (left) and inter-observer (right) variability for the old counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) for the four parameters (V, N, Q_A, A_A). In the first row this is displayed for the number of vessel profiles in a region of interest (N). In the second row this is displayed for the microvessel density (Q_A). In the third row this is displayed for the number of points in the grid hitting a vessel profile in a region of interest (V). In the last row this is displayed for the areal fraction of vessel profiles (A_A). In addition are the ICCs in relation to the heterogeneity level (low or high) and the cancer type (colorectal carcinoma (CRC), glioblastoma multiforme (GBM), ovarian carcinoma (OC), and renal cell carcinoma (RCC)) shown.</p

Inter-observer variability for the old counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) between the averaged counting of observer 1 (KM) and 2 (VC).</p

Intra-observer variability for the old counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) between the counting of round one and two of observers 1 and 2 (ICC1 and ICC2) for the four different cancer types and the four different parameters.</p

Inter-observer variability for the new counting rules.

<p>This was calculated by the intraclass correlation coefficients (ICC) between the counting of observer 1 (KM), 4 (PV), 5 (YW) and 6 (WW).</p

Inter-observer variation for the old counting rules between observer 1 (KM) and 2 (VC) for colorectal cancer samples.

<p>This was displayed by Bland-Altman (a, c, e, g) and prediction plots with prediction intervals (two black lines) (b, d, f, h) for the number of vessel profiles (N) (a, b), the microvessel density (Q_A) (c,d), the number of points in the grid hitting a vessel profile (V) (e, f) and the areal fraction of vessel profiles (A_A) (g, h). A systemic bias for N, Q_A, and A_A was present as illustrated by the prediction plots (large distance between the x = y line (black and dashed) and the linear regression line of the measurements (red)).</p

Example of a region of interest.

<p>It was captured in Pannoramic Viewer (3DHISTECH, Budapest, Hungary) and combined with a digital 81-points grid in Adobe Photoshop CS4. CD31-stained vessel profiles in the grid were counted as N (green arrow). Vessel profiles that cross the virtually extended left or lower line of the grid were not counted (shaded green arrow). The grid points that hit a CD31-stained vascular profile were counted as V (red arrow). Scale bar represents 100 μm.</p

Results of different vessel counting methods in four cancer types.

<p>Studies differ in staining of the vessels, number of ROIs, magnification, number of observers and study size.</p

Tukey boxplots illustrating the relationship between the mean minimum number of regions of interest (ROIs) and the topological blood vessel heterogeneity.

<p>This was done for each sample and for every cancer type: colorectal carcinoma (CRC), glioblastoma multiforme (GBM), ovarian carcinoma (OC), and renal cell carcinoma (RCC). If the topological blood vessel heterogeneity of the samples increased (low < high), the minimum number of ROIs on average increased as well. ***p < 0.001, **p < 0.01, *p < 0.05.</p