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Table 4 The selected features with the best performance for LNM

From: Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Number Sequence Feature
1 ADC Variance
2 ADC S(1,1)Correlat
3 ADC S(2,-2)SumVarnc
4 ADC S(0,4)Entropy
5 ADC S(5,0)SumAverg
6 ADC Teta3
7 ADC WavEnLH_s-4
8 DWI Variance
9 DWI S(2,0)DifEntrp
10 DWI S(3,-3)SumOfSqs
11 DWI S(4,0)Contrast
12 DWI S(5,0)Entropy
13 DWI 45dgr_LngREmph
14 DWI WavEnLL_s-4
15 T1WI S(3,0)SumAverg
16 T1WI S(3,3)SumVarnc
17 T1WI S(0,5)SumEntrp
18 T1WI S(5,-5)SumOfSqs
19 T1WI S(5,-5)DifVarnc
20 T1WI Vertl_RLNonUni
21 T1WI WavEnLL_s-1
22 T1WI WavEnHH_s-1
23 T2WI Skewness
24 T2WI S(0,1)DifVarnc
25 T2WI S(2,0)SumAverg
26 T2WI S(2,2)InvDfMom
27 T2WI S(3,0)SumOfSqs
28 T2WI S(5,-5)DifVarnc
29 T2WI WavEnLH_s-2
30 T2WI WavEnHH_s-4
  1. Axial T1-precontrast weighted imaging, T1WI; axial T2-weighted imaging, T2WI; axial diffusion weighted imaging, DWI; Apparent diffusion coefficient, ADC