Table 2. First order statistical features used for texture analysis.

Feature Name	Equation	#
Coefficient of variation, CV	$=\frac{{\sigma }_c}{{\mu }_c}$	3
Excess kurtosis	$=\frac{1}{N}{\displaystyle \sum _{l=1}^N}{\left(\frac{c_l-{\mu }_c}{{\sigma }_c}\right)}^4-3$	4
Skewness	$=\frac{1}{N}{\displaystyle \sum _{l=1}^N}{\left(\frac{c_l-{\mu }_c}{{\sigma }_c}\right)}^3$	5
Lilliefor’s D	The test statistic from a Kolmogorov-Smirnov test comparing the VOI value cumulative distribution Mc to a cumulative Gaussian G0 with same μc and σc: $D=\Vert M_c-G_0\Vert$	6
Lilliefor’s p	Probability, that an observed AC distribution conforms to a Gaussian calculated from the test statistic D [44].	7
Gauss Fit NRMSD	The normalized room mean squared deviation (NRMSD) of a Gaussian fitted to the AC distribution in the VOI	8
Crest factor, CF (dB)	$CF=20{\log }_{10}\frac{\left|c_{max}\right|}{RMS\left(\mathit{c}\right)},RMS\left(c\right)=\sqrt{\frac{1}{N}\sum {\mathit{c}}^2}$	9
Efficiency, normalized entropy, E	$E={\log }_N\left({\displaystyle \prod }_{i=1}^{N}P{\left(i\right)}^{-P\left(i\right)}\right)$, P(i) = relative frequency of voxels with gray level i in c.	10
Mode / Mean	Ratio of the mode and the mean activity concentration found in the VOI	11