Construction of the radiomics model
REGRESSION
To reduce redundant features and mitigate overfitting in the developed radiomics model, the following steps were undertaken for the features in the training set: (1) Features with an ICC greater than 0.75 were selected, (2) Univariate logistic analysis was conducted to identify the features that exhibited statistical significance, (3) The most significant features were chosen through a Gradient Boosting Decision Tree (GBDT) and further validated through multivariate logistic analysis, and (4) The remaining features were utilized to compute the radiomics score (Rad-score). Subsequently, a radiomics model was constructed in the training set using multivariate logistic regression. In order to assess the model’s performance, a separate radiomics model was built in the test set for testing purposes.