Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma

Table 3 Performance of radiomics models for predicting glypican-3 expression status in hepatocellular carcinoma

Set	Model	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Accuracy
Training set	LR	0.889	0.814-0.963	0.643	0.932	0.857	0.804	0.819
	SVM	0.949	0.903-0.995	0.857	0.932	0.889	0.911	0.903
	KNN	0.853	0.773-0.934	0.750	0.773	0.677	0.829	0.764
	RF	0.959	0.921-0.997	0.964	0.818	0.771	0.973	0.875
	ET	0.953	0.900-1.000	0.893	0.909	0.862	0.930	0.903
	XGBoost	1.000	1.000-1.000	0.964	1.000	1.000	0.978	0.986
	LightGBM	0.933	0.879-0.987	0.857	0.841	0.774	0.902	0.847
	MLP	0.900	0.827-0.973	0.786	0.864	0.786	0.864	0.833
Testing set	LR	0.759	0.277-1.000	0.500	0.517	0.067	0.937	0.516
	SVM	0.707	0.190-1.000	0.000	0.966	0.000	0.933	0.903
	KNN	0.586	0.000-1.000	0.000	1.000	0.000	0.935	0.935
	RF	0.862	0.638-1.000	0.705	0.759	0.725	0.957	0.742
	ET	0.819	0.515-1.000	0.500	0.690	0.100	0.952	0.677
	XGBoost	0.759	0.277-1.000	0.500	0.517	0.067	0.937	0.516
	LightGBM	0.741	0.291-1.000	0.500	0.552	0.071	0.941	0.548
	MLP	0.655	0.000-1.000	0.000	1.000	0.000	0.935	0.935

LR: Logistic regression; SVM: Support vector machine; KNN: K-nearest neighbors; RF: Random forest; ET: Extra trees; MLP: Multilayer perceptron; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; XGBoost: Extreme gradient boosting; LightGBM: Light Gradient Boosting Machine.