Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

Table 1 Comparison of clinical data between the two groups

Clinical data	Training set		t/Z/χ2	P value	Validation set		t/Z/χ2	P value
	No lymph node metastasis (n = 141)	Lymph node metastasis (n = 228)			No lymph node metastasis (n = 51)	Lymph node metastasis (n = 72)
Gender			1.017	0.313			1.126	0.289
Male	99 (70.2)	171 (75.0)			33 (64.7)	53 (73.6)
Female	42 (29.8)	57 (25.0)	0		18 (35.3)	19 (26.4)
Age (yr)			0.015	0.901			4.729	0.030
≤ 60	64 (45.4)	105 (46.1)			27 (52.9)	24 (33.3)
> 60	77 (54.6)	123 (53.9)			24 (47.1)	48 (66.7)
Mode of operation			7.816	0.005			3.578	0.059
Partial gastrectomy	113 (80.1)	152 (66.7)			43 (84.3)	50 (69.4)
Total gastrectomy	28 (19.9)	76 (33.3)			8 (15.7)	22 (30.6)
Tumor invasion depth			-11.022	< 0.001			-7.114	< 0.001
T1	61 (43.3)	13 (5.7)			30 (58.8)	4 (5.6)
T2	42 (29.8)	22 (9.6)			13 (25.5)	13 (18.1)
T3	21 (14.9)	64 (28.1)			6 (11.8)	27 (37.5)
T4	17 (12.1)	129 (56.6)			2 (3.9)	28 (38.9)
Tumor site			0.716	0.699			0.392	0.822
Gastric body	24 (17.0)	32 (14.0)			18 (35.3)	22 (30.6)
Gastric antrum	73 (51.8)	126 (55.3)			26 (51.0)	38 (52.8)
Gastric cardia	44 (31.2)	70 (30.7)			7 (13.7)	12 (16.7)
Nerve or vascular invasion			128.649	< 0.001			54.772	< 0.001
No	108 (76.6)	39 (17.1)			42 (82.4)	11 (15.3)
Yes	33 (23.4)	189 (82.9)			9 (17.6)	61 (84.7)
Maximum tumor diameter			38.634	< 0.001			8.323	0.004
≤ 5 cm	122 (86.5)	126 (55.3)			46 (90.2)	49 (68.1)
> 5 cm	19 (13.5)	102 (44.7)			5 (9.8)	23 (31.9)
PIV	132.00 (80.73, 226.80)	190.72 (106.49, 311.44)	-3.606	< 0.001	149.43 (91.73, 217.49)	173.59 (102.20, 274.73)	-1.586	0.113
CEA	2.47 (1.53, 3.58)	2.90 (1.82, 6.87)	-3.189	0.001	2.65 (1.47, 3.95)	4.91 (1.97, 9.02)	-2.331	0.020

PIV: Pan-immune-inflammation value; CEA: Carcinoembryonic antigen.

Table 2 Prediction performance evaluation of each model

Model	AUC	Accuracy	Kappa	Sensitivity (recall rates)	Specificity
DT	0.824	0.821	0.638	0.806	0.843
RF	0.923	0.854	0.702	0.847	0.882
SVM	0.721	0.585	0.000	0.750	0.547
GBM	0.927	0.870	0.734	0.875	0.863
NB	0.914	0.821	0.640	0.861	0.843
MLP	0.907	0.837	0.665	0.882	0.824
LR	0.898	0.821	0.636	0.806	0.882

AUC: Area under the receiver operating characteristic curve; DT: Decision tree; RF: Random forest; SVM: Support vector machine; GBM: Gradient boosting machine; NB: Naive bayes; LR: Logistic regression; MLP: Multi-layer perceptron.