Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation

Table 2 Predictive performance of seven machine learning models of all classifiers across different feature sets, area under the curve (95% confidence interval)

		KNN	SVM	LR	DT	GBDT	RF	XGBoost
Clinical + rad-preop	Training set	0.966 (0.947-0.984)	0.928 (0.893-0.964)	0.941 (0.91-0.973)	0.757 (0.701-0.814)	0.973 (0.954-0.992)	0.909 (0.871-0.946)	0.987 (0.974-1)
	Internal validation set	0.76 (0.631-0.888)	0.848 (0.936-0.98)	0.867 (0.772-0.963)	0.756 (0.64-0.87)	0.845 (0.799-0.892)	0.76 (0.636-0.884)	0.864 (0.817-0.91)
	External validation set	0.752 (0.622-0.883)	0.849 (0.747-0.951)	0.84 (0.735-0.945)	0.716 (0.597-0.836)	0.825 (0.715-0.935)	0.752 (0.625-0.878)	0.83 (0.72-0.939)
Clinical + rad-preop + peri-necrotic (5 mm)	Training set	0.97 (0.954-0.987)	0.957 (0.933-0.982)	0.963 (0.94-0.986)	0.757 (0.701-0.814)	0.986 (0.974-0.999)	0.917 (0.88-0.953)	0.993 (0.984-1)
	Internal validation set	0.938 (0.911-0.964)	0.894 (0.852-0.935)	0.865 (0.819-0.911)	0.751 (0.618-0.883)	0.933 (0.898-0.967)	0.839 (0.788-0.89)	0.839 (0.928-0.985)
	External validation set	0.81 (0.692-0.928)	0.863 (0.761-0.965)	0.859 (0.752-0.966)	0.716 (0.597-0.836)	0.819 (0.706-0.932)	0.824 (0.709-0.94)	0.816 (0.702-0.93)
Clinical + rad-preop + peri-necrotic (10 mm)	Training set	0.952 (0.929-0.975)	0.942 (0.912-0.972)	0.95 (0.921-0.979)	0.757 (0.701-0.814)	0.973 (0.953-0.993)	0.913 (0.875-0.951)	0.987 (0.975-1)
	Internal validation set	0.833 (0.717-0.949)	0.898 (0.859-0.936)	0.898 (0.859-0.937)	0.736 (0.678-0.794)	0.907 (0.878-0.943)	0.803 (0.688-0.919)	0.904 (0.869-0.939)
	External validation set	0.818 (0.7-0.935)	0.876 (0.784-0.969)	0.873 (0.775-0.971)	0.716 (0.597-0.836)	0.871 (0.773-0.969)	0.795 (0.674-0.917)	0.855 (0.749-0.961)
Clinical + rad-preop + peri-necrotic (15 mm)	Training set	0.96 (0.94-0.98)	0.941 (0.908-0.974)	0.95 (0.919-0.981)	0.757 (0.701-0.814)	0.976 (0.958-0.994)	0.913 (0.876-0.95)	0.985 (0.97-1)
	Internal validation set	0.927 (0.893-0.96)	0.875 (0.784-0.967)	0.873 (0.777-0.9969)	0.725 (0.599-0.851)	0.913 (0.876-0.951)	0.833 (0.72-0.946)	0.839 (0.74-0.937)
	External validation set	0.851 (0.747-0.954)	0.863 (0.763-0.962)	0.856 (0.754-0.958)	0.716 (0.597-0.836)	0.84 (0.734-0.946)	0.815 (0.7-0.93)	0.833 (0.727-0.939)
Clinical + rad-preop + peri-necrotic (20 mm)	Training set	0.971 (0.955-0.987)	0.986 (0.974-0.997)	0.958 (0.934-0.982)	0.767 (0.711-0.823)	0.927 (0895-0.959)	0.918 (0.885-0.952)	0.995 (0.99-1)
	Internal validation set	0.771 (0.712-0.831)	0.792 (0.67-0.913)	0.79 (0.732-0.849)	0.742 (0.684-0.8)	0.801 (0.676-0.926)	0.81 (0.691-0.929)	0.834 (0.72-0.948)
	External validation set	0.717 (0.583-0.851)	0.795 (0.676-0.915)	0.773 (0.645-0.9)	0.664 (0.537-0.791)	0.763 (0.637-0.889)	0.753 (0.625-0.881)	0.782 (0.654-0.91)

KNN: K-nearest neighbors; SVM: Support vector machine; LR: Logistic regression; DT: Decision tree; GBDT: Gradient boosting decision tree; RF: Random forest; XGBoost: Extreme gradient boosting.