Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study

Table 2 The six best models with optimal machine learning approach and their performance in training and internal validation set

Model	Training set				Internal-Validation set
	Accuracy	AUC (95%CI)	NPV	PPV	Accuracy	AUC (95%CI)	NPV	PPV
The tumor-only model (KNN)	0.690	0.757 (0.724-0.789)	0.532	0.796	0.731	0.750 (0.700-0.801)	0.654	0.780
The 5 mm peri-tumor model (LightGBM)	0.768	0.857 (0.824-0.891)	0.629	0.860	0.746	0.713 (0.665-0.761)	0.577	0.854
The 10 mm peri-tumor model (KNN)	0.723	0.802 (0.776-0.829)	0.548	0.839	0.776	0.803 (0.763-0.842)	0.577	0.902
The tumor-peri-tumor 5 mm model (extreme gradient boosting)	0.935	0.987 (0.934-1.000)	0.887	0.968	0.701	0.774 (0.696-0.853)	0.654	0.732
The tumor-peri-tumor 10 mm model (logistic regression)	0.781	0.862 (0.821-0.904)	0.774	0.785	0.761	0.841 (0.777-0.904)	0.692	0.805
The model that incorporates features from all regions—tumor, 5 mm, and 10 mm (LightGBM)	0.832	0.924 (0.890-0.958)	0.726	0.903	0.776	0.899 (0.850-0.948)	0.615	0.878

AUC: Area under the receiver operating characteristic curve; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine; NPV: Negative predictive value; PPV: Positive predictive value.