Development of RF model and logistic regression
Stage T
REGRESSION, EVENT
Based on the R package “randomForest”, RF models for predicting the probability of the event in the 1-year cohort and 5-year cohort were developed, respectively. Firstly, in the 1-year cohort, 225 patients were divided into the training set while 56 were into the test set. After measurements, Stage T, stage N, HR, HER2, and P53 were selected as explanatory variables. When mtry = 5 (Fig. The choice of the number of mtry and ntree in the 1-year cohort (The mean decrease accuracy and mean decrease gini index in the 1-year cohort (Secondly, the model was tested in the test set. The ROC of the RF model was performed and the specificity, sensitivity, and AUC were 0.800 (95% CI was 0.659–0.895), 0.833 (95% CI was 0.365–0.991), and 0.810 (95% CI was 0.640–0.980, Fig. The ROC curve of the random forest model: training set and test set of the 1-year cohort (The ROC curve of the logistic regression: training set and test set of the 1-year cohort (Next, we used the same method to establish the RF model and logistic regression in the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. cT, HR, and HER2 were selected as explanatory variables. Figure The specificity, sensitivity, AUC, PPV, NPV and F1 score of the train set and test set in 1-year cohort and 5-year cohort, respectivelyFinally, in the 5-year cohort test set, we categorized patients into the low risk group if the probability of no event occurring exceeds 50%, and into the high risk group otherwise. Afterwards, we proceeded to plot survival curves for both groups and observed a clear distinction: the low-risk group exhibited significantly longer survival times in comparison to the high-risk group (Fig. The DFS curve for both the low risk group and high risk group predicted by the model