Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study

Table 2 The dominant texture features selected after microvascular invasion dimensionality reduction

T2WI	AP	VP	DP	Multiparametric
GrSkewness	Z_ShrtREmp	Z_LngREmph	Perc.01% 3D	AP-S (0, 1, 0) SumAverg
135dr_GLevNonU	Z_Fraction	Z_Fraction	S (0, 0, 1) InvDfMom	AP-Horzl_Fraction
45dgr_GLevNonU	Z_LngREmph	S (1, 0, 0) SumAverg	GrMean	AP-Horzl_ShrtREmp
Horzl_GLevNonU	45dgr_ShrtREmp	S (0, 0, 1) SumAverg	Z_LngREmph	AP-Horzl_LngREmph
Z_RLNonUni	S (0, 0, 1) InvDfMom	S (1, 1, 0) SumAverg	Z_ShrtREmp	AP-S (0, 0, 1) InvDfMom
135dr_ShrtREmp	S (0, 0, 1) SumAverg	Vertl_RLNonUni	S (1, -1, 0) SumAverg	VP- 45dgr_RLNonUni
Horzl_Fraction	Skewness 3D	135dr_RLNonUni	S (1, -1, 0) SumVarnc	VP-Vertl_RLNonUni
135dr_RLNonUni	GrSkewness	Z_ShrtREmp	Z_Fraction	VP-S (0, 0, 1) SumAverg
Z_GLevNonU	45dgr_Fraction	Perc.10% 3D	S (0, 1, 0) SumVarnc	VP-Z_ShrtREmp
Vertl_GLevNonU	Kurtosis 3D	S (0, 1, 0) SumAverg	Perc.10% 3D	VP-Z_LngREmph

T2WI: T2-weighted imaging; 3D: Three-dimensional; AP: Arterial phases; VP: Venous phase; DP: Delayed phase.