Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

Table 3 Comparison of radiomic models based on various machine learning methods

Radiomic model		Accuracy	AUC	95%CI	Sensitivity	Specificity	PPV	NPV
LR	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.778	0.786	0.4783-1.0000	0.5	0.857	0.5	0.857
SVM	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.722	0.75	0.4478-1.0000	0.5	0.786	0.4	0.846
KNN	Train	0.81	0.897	0.8034-0.9913	0.545	0.903	0.667	0.848
	Test	0.778	0.821	0.6369-1.0000	0.5	0.857	0.5	0.857
RF	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.833	0.679	0.2120-1.0000	0.25	1	1	0.824
Extra trees	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.722	0.616	0.2080-1.0000	0.25	0.857	0.333	0.8
XGBoost	Train	0.976	1	1.0000-1.0000	0.909	1	1	0.969
	Test	0.833	0.75	0.4352-1.0000	0.25	1	1	0.824
MLP	Train	0.952	0.997	0.9889-1.0000	0.909	0.968	0.909	0.968
	Test	0.722	0.768	0.4914-1.0000	0.5	0.786	0.4	0.846

NPV: Negative predictive value; PPV: Positive predictive value; CI: Confidence interval; AUC: Area under the curve; LR: Logistic regression; KNN: K-nearest neighbor classification; RF: Random forest; XGBoost: EXtreme gradient boosting; MLP: Multilayer perceptron; SVM: Support vector machine.