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

Table 1 Clinical characteristics of all esophageal cancer patients

Characteristics	Non-response group	Response group	P value
Age (year)	60.38 ± 8.96	55.44 ± 7.99	0.347
Gender, n (%)			0.375
Male	78 (81.25)	31 (86.11)
Female	18 (18.75)	5 (13.89)
Location, n (%)			0.765
Upper	17 (17.71)	4 (11.11)
Middle	46 (47.92)	20 (55.56)
Lower	23 (23.95)	9 (25.00)
Middle and lower	10 (10.42)	3 (8.33)
Degree of differentiation, n (%)			0.388
Well differentiated	1 (1.04)	1 (2.78)
Moderately differentiated	74 (77.08)	29 (80.56)
Poorly differentiated	14 (14.59)	5 (13.88)
Medium to high differentiation	1 (1.04)	0 (0)
Middle to low differentiation	6 (6.25)	1 (2.78)
Length of tumor	5.54 ± 1.76	5.43 ± 1.91	0.862
CEA	3.09 (1.78, 3.26)	2.89 (1.74, 3.65)	0.47
SCCA	2.40 (0.80, 3.00)	1.70 (0.74, 1.86)	0.859

CEA: Carcinoembryonic antigen; SCCA: Squamous cell carcinoma antigen.

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.

Table 3 Delong test for comparing area under the curves among classification algorithms

Models	LR	SVM	KNN	RF	ET	XGBoost	LGBM	AdaBoost	MLP
LR		0.405	0.900	0.480	0.194	0.794	1.000	1.000	0.820
SVM	0.405		0.729	0.194	0.110	0.852	0.648	0.546	0.863
KNN	0.900	0.729		0.637	0.095	0.893	0.895	0.859	0.873
RF	0.480	0.194	0.637		0.230	0.582	0.710	0.626	0.648
ET	0.194	0.110	0.095	0.230		0.230	0.275	0.160	0.246
XGBoost	0.794	0.852	0.893	0.582	0.230		0.480	0.745	1.000
LGBM	1.000	0.648	0.895	0.710	0.275	0.480		1.000	0.777
AdaBoost	1.000	0.546	0.859	0.626	0.160	0.745	1.000		0.794
MLP	0.820	0.820	0.873	0.648	0.246	1.000	0.777	0.794

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; AdaBoost: Adaptive boosting; MLP: Multilayer perceptron.