Table 2.

MRI appearance features characterising abnormalities for outcome prediction.

Categories	Details of features
I. Anatomy of abnormalities	I.1. Mass centre in standard atlas space; I.2. Percentage of the whole-brain volume and the volume of each of the 61 auto-segmented brain structures being injured39 41; I.3. Ratios of volumetric injury in the same brain structures between the left and right hemisphere; I.4. Percentage and distribution of abnormalities in 28 major fibre tracts as defined in the JHU atlas.145
II. Geometry of abnormalities	II.1. Volume of abnormal regions; II.2. Maximum diameter along different orthogonal directions, maximum surface of abnormal regions, geometric compactness, spherecity, surface-to-volume ratio in the abnormal regions.
III. Heterogeneity of abnormalities	III.1. Histogram analysis (0, 25, 50, 75 and 100-percentile) of T1w, T2w, T1-Gad, FLAIR, ADC, SWI, ZT1w, ZT2w, ZFLAIR, ZADC, ZSWI signal values within the abnormal regions; III.2. Skewness (asymmetry), kurtosis (flatness), and randomness (entropy, SD) of T1w, T2w, T1-Gad, FLAIR, ADC, SWI, ZT1w, ZT2w, ZFLAIR, ZADC, ZSWI signal values within abnormal regions;
IV. Texture of abnormalities	IV.1. Gray-level co-occurrence matrix features and gray-level run-length matrix of T1w, T2w, T1-Gad, FLAIR, ADC, SWI, ZT1w, ZT2w, ZT1-Gad, ZFLAIR, ZADC, ZSWI signal values within abnormal regions; IV.2. fractal analysis, Minkowski functionals, wavelet transform and Laplacian transforms of Gaussian-filtered images for the abnormal regions.

ADC, apparent diffusion coefficient; FLAIR, fluid-attenuated inversion recovery; SWI, susceptibility-weighted imaging.