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

Table 2 The features selected from feature subset 1 using least absolute shrinkage and selection operator, with the Cox regression analysis

Covariate	HR (95%CI)	P value
clinical_T	1.90 (1.27-2.84)	0.002
clinical_N	1.32 (1.08-1.61)	0.006
clinical_M	2.04 (1.30-3.18)	0.002
ECOG	1.60 (1.38-2.02)	0.024
sarco0	1.42 (1.35-1.50)	0.044
TATSMR1	1.23 (0.93-1.33)	0.047
delta_SAT	1.00 (1.00-1.37)	0.024

HR: Hazard ratio; CI: Confidence interval; ECOG: Eastern Cooperative Oncology Group; sarco: Sarcopenia; 0: Pretreatment; TATSMR1: Total adipose tissue index/skeletal muscle index ratio; 1: Follow-up; SAT: Subcutaneous adipose tissue.