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

Figure 1 Flowchart of patient selection and data processing. UEGC: Undifferentiated early gastric cancer; EMR: Endoscopic mucosal resection; ESD: Endoscopic submucosal dissection; RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting; ROC: Receiver operating characteristic; DCA: Decision curve analysis; CIC: Clinical impact curve; LNM: Lymph node metastasis.

Figure 2 Variable screening and weight allocation. A: Correlation matrix analysis of candidate features; B: Weight distribution of candidate variables for each mL based model. RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting.

Figure 3 Visualization model prediction based on machine learning based algorithm. A: Random forest classifier model; B: Decision tree model. Candidate factors associated with fracture risk are named through random forest classifier algorithm, and prediction nodes and weights are assigned by the decision tree algorithm.

Figure 4 Visualization of prediction models based on artificial neural network algorithm. A: Artificial neural network model; B: Importance of variables using connection weights. Candidate factors associated with lymph node metastasis are ordered via artificial neural network (ANN) algorithm and prediction nodes, and weights are assigned via an ANN algorithm. IV_0: Inertia value 0°; IV_45: Inertia value 45°; IG_0: Inverse gap 0°; IG_45: Inverse gap 45°; IG_all: Inverse gap full angle; Haralick_30: Haralick 30°; Haralick_all: Haralick full angle.

Figure 5 Predictive performance of candidate models based on machine learning based algorithm. A: Decision curve analysis (DCA) for five mL based models in training sets; B: DCA for five ml based models in test sets. RFC: Random forest classifier; SVM: Support vector machine; DT: Decision tree; ANN: Artificial neural network; XGboost: Extreme gradient boosting.