Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer

Table 2 Receiver operating characteristic curve analysis of lymph node metastasis in each mL based model

Model	Training set		Testing set		Variables1
	AUC mean	AUC 95%CI	AUC mean	AUC 95%CI
RFC	0.925	0.378-1.472	0.912	0.355-1.469	Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, IV_45
ANN	0.887	0.340-1.434	0.837	0.280-1.394	Entropy, IG_all, IG_0, IG_45, IG_90, IV_all, IV_all_SD, IV_0, IV_45, Haralick_all, Haralick_30
DT	0.856	0.309-1.403	0.813	0.256-1.370	Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, IV_45
XGboost	0.814	0.267-1.361	0.807	0.250-1.364	Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, IV_45, IG_90
SVM	0.805	0.258-1.352	0.794	0.237-1.351	Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, IV_45
GLM	0.796	0.229-1.362	0.799	0.233-1.365	Entropy, Haralick_all, Haralick_30, IG_all, IG_45, IG_0, IV_45
Radiologist	0.789	0.242-1.336	0.801	0.254-1.348	-

¹Variables are included in the model.

RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting; GLM; Generalized linear model; AUC: Area under the receiver operating characteristic curve; 95%CI: 95% confidence interval; IV_0: Inertia value 0°; IV_45: Inertia value 45°; IV_90: Inertia value 90°; IG_0: Inverse gap 0°; IG_45: Inverse gap 45°; IG_90: Inverse gap 90°; IG_all: Inverse gap full angle; Haralick_30: Haralick 30°.