Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography

Table 5 The features selected from feature subset 4 using least absolute shrinkage and selection operator, with the Cox regression analysis

Covariate	HR (95%CI)	P value
clinical_M	2.13 (1.26-3.60)	0.005
clinical_T	1.91 (1.23-2.95)	0.004
MUSCLE_original_glszm_LargeAreaEmphasis1	1.00 (1.00-1.00)	0.003
MUSCLE_original_shape_Sphericity0	1.02 (1.00-1.03)	0.049
SAT_original_firstorder_Skewness0	0.71 (0.46-0.99)	0.041
VAT_original_firstorder_10Percentile1	1.01 (1.00-1.03)	0.032
VAT_original_firstorder_90Percentile1	1.11 (1.04-1.19)	0.002
VAT_original_firstorder_Mean1	1.04 (1.01-1.07)	0.002
VAT_original_glcm_JointEnergy1	12.19 (0.38-394.85)	0.042
VAT_original_gldm_DependenceVariance1	0.94 (0.91-0.98)	0.002
VAT_original_glrlm_LongRunLowGrayLevelEmphasis1	0.00 (0.00-2.39)	0.049
VAT_original_glrlm_RunEntropy1	0.09 (0.02-0.37)	0.001
VAT_original_glszm_GrayLevelNonUniformityNormalized1	5606.87 (0.12-270661365.71)	0.041
VAT_original_shape_SurfaceVolumeRatio0	1.76 (0.97-3.21)	0.045
delta_VAT_original_shape_Sphericity1	0.24 (0.06-1.02)	0.042

HR: Hazard ratio; CI: Confidence interval; VAT: Visceral adipose tissue; glcm: Gray-level co-occurrence matrix; glszm: Gray-level size zone matrix; glrlm: Gray-level run length matrix; gldm: Gray-level dependence matrix; 0: Pretreatment; 1: Follow-up.