Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics

Table 2 Performance of different classification algorithms in predicting neoadjuvant therapy efficacy in esophageal cancer

Models	Task	AUC	95%CI	Sensitivity	Specificity	Accuracy
LR	Train	0.798	0.7127-0.8841	0.844	0.634	0.693
LR	Test	0.800	0.5144-1.0000	0.667	0.600	0.615
SVM	Train	0.735	0.6310-0.8393	0.781	0.598	0.649
SVM	Test	0.733	0.3511-1.0000	0.333	0.800	0.692
KNN	Train	0.848	0.7814-0.9148	0.375	0.915	0.763
KNN	Test	0.783	0.4851-1.0000	0.667	0.700	0.692
RF	Train	0.962	0.9321-0.9910	0.906	0.854	0.868
RF	Test	0.833	0.5562-1.0000	0.667	0.600	0.615
ET	Train	0.932	0.8832-0.9811	0.906	0.817	0.842
ET	Test	0.900	0.6801-1.0000	0.667	0.700	0.692
XGBoost	Train	1.000	1.0000-1.0000	0.969	1.000	0.991
XGBoost	Test	0.767	0.4999-1.0000	0.667	0.700	0.692
LGBM	Train	1.000	1.0000-1.0000	0.969	1.000	0.991
LGBM	Test	0.800	0.5507-1.0000	0.667	0.700	0.692
AdaBoost	Train	1.000	1.0000-1.0000	0.969	1.000	0.991
AdaBoost	Test	0.800	0.5203-1.0000	0.667	0.600	0.615
MLP	Train	0.808	0.7179-0.8987	0.781	0.805	0.798
MLP	Test	0.767	0.4938-1.0000	0.667	0.700	0.692

AUC: Area under the curve; LR: Logistic regression; SVM: Support vector machines; KNN: K-nearest neighbor; RF: Random forest; ET: ExtraTrees; XGBoost: Extreme gradient boosting; LGBM: Light gradient boosting machine; MLP: Multilayer perceptron; AdaBoost: Adaptive boosting.