Mean (and standard deviation) of the CV of the 20 patients’ regarding each dynamic range considered.

<p>CV was computed for each feature considering different combinations of matrix size and slice thickness, that is, matrix sizes of 432x432 and 256x256 pixels and slice thickness of 1 mm and 2 mm. Shaded cells correspond to those combinations obtaining a CV below 10%.</p

Values of several textural features (normalized to the maximum value obtained in each subplot) for different spatial resolutions (432x432 ST 1mm, 432x432 ST 2 mm, 256x256 ST 1 mm, 256x256 ST 2 mm) and dynamic range values (16, 32 and 64 grey levels).

<p>Shown are results for a) co-occurence (CM) Entropy, b) CM Homogeneity, c) run-length matrix (RLM) SRE, d) RLM LRE.</p

Mean (and standard deviation) of the CV computed for the 20 patients.

<p>Results are shown for each combination of spatial resolution and slice thickness considered. CV was computed for each feature considering different dynamic range values, i.e. 16, 32 and 64 grey levels.</p

Definition of the heterogeneity measures computed in this study.

<p>Definition of the heterogeneity measures computed in this study.</p

Summary of patient characteristics, MRI data and volumetric parameters for the 40 patients included in the study.

<p>Summary of patient characteristics, MRI data and volumetric parameters for the 40 patients included in the study.</p

Overall survival, <i>G</i>_<i>H</i>, <i>V</i>_<i>I</i> and MGMT status (when available) for all of the patients included in the study.

<p>Within each group (BVZ+ or BVZ-) patients are ordered by survival. Grey cells indicate patients in the favourable groups: either small <i>G</i>_<i>H</i> (dark grey) or unmethilated MGMT (light grey).</p

Spearman correlation coefficients between every pair of significant variables in our study.

<p>Boldfaced numbers indicate significant correlations (p<0.05).</p

Comparison of classical prognostic biomarkers values between the different patient subgroups.

<p>Comparison of classical prognostic biomarkers values between the different patient subgroups.</p

Visual examples of two tumors with different geometric heterogeneities.

<p>The first case corresponds to a rim-heterogeneous tumor (Fig 1A and 1B), having large geometric heterogeneity. The second case is a geometrically regular tumor (Fig 1C and 1D), with smaller geometric heterogeneity.</p

Summary of univariate Cox and Kaplan-Meier analysis for the more representative variables included in the study.

<p>Significant results are boldfaced.</p