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

Table 7 Model performances across different input combinations for 1-year, 2-year, and 3-year survival prediction

Input	Models	1-year			2-year			3-year
		AUC	Sensitivity	Specificity	AUC	Sensitivity	Specificity	AUC	Sensitivity	Specificity
Subset 1: No-radiomics	SVC	0.691	0.46	0.83	0.73	0.70	0.66	0.64	0.73	0.51
	LR	0.67	0.58	0.73	0.841	0.73	0.82	0.66	0.63	0.68
	ETC	0.66	0.50	0.78	0.77	0.65	0.78	0.71	0.88	0.45
	Nomogram	0.67	0.51	0.76	0.70	0.86	0.44	0.711	0.65	0.71
Subset 2: Pretreatment	SVC	0.66	0.81	0.47	0.73	0.82	0.54	0.68	0.75	0.52
	LR	0.65	0.67	0.56	0.771	0.70	0.70	0.761	0.67	0.80
	ETC	0.64	0.65	0.54	0.76	0.80	0.56	0.68	0.49	0.85
	Nomogram	0.711	0.77	0.61	0.69	0.74	0.54	0.69	0.82	0.47
Subset 3: Follow-up	SVC	0.60	0.55	0.65	0.74	0.69	0.67	0.53	0.68	0.45
	LR	0.61	0.62	0.60	0.811	0.71	0.80	0.53	0.58	0.59
	ETC	0.60	0.56	0.63	0.76	0.78	0.61	0.64	0.61	0.64
	Nomogram	0.701	0.73	0.59	0.71	0.71	0.64	0.701	0.75	0.61
Subset 4: Combination	SVC	0.60	0.42	0.79	0.84	0.63	0.82	0.59	0.47	0.66
	LR	0.61	0.80	0.36	0.911	0.81	0.88	0.55	0.68	0.54
	ETC	0.60	0.51	0.70	0.81	0.62	0.85	0.64	0.62	0.64
	Nomogram	0.731	0.85	0.52	0.73	0.64	0.69	0.741	0.72	0.67

¹The highest area under the curve values within each feature subset for each time point.

AUC: Area under the time-dependent receiver operating characteristic curve; SVC: Support vector classifier; LR: Logistic regression; ETC: Extra trees classifier.