Table 1.

The selected radiomic features and relevant coefficients in radiomics model

Radiomic features	Regression coefficient
original shape Sphericity	-0.2654
wavelet.HHL glcm Maximum Probability	-0.08488
wavelet.LLL firstorder Median	-0.004219
logarithm glszm large area low gray level emphasis	0.4804

Note: The most useful predictive features were selected by using the least absolute shrinkage and selection operator (LASSO) method. Briefly, the optimized hyperparameter λ was first determined by using 10-fold cross validation with binomial deviance as criterion. Then the features with non-zero coefficient were selected based on the determined optimal λ. Finally, LASSO logistic regression was conducted to construct the radiomics model and a radiomics score (Rad-score) was calculated for each patient via a linear combination of selected and weighted features by their corresponding coefficients. The equations of the Rad-score and the probability of RM in clinical and radiomics model are: Radscore=-0.2654*original shape Sphericity-0.08488*wavelet.HHL glcm Maximum Probability-0.004219*wavelet.LLL firstorder Median+o.4804*logarithmglszm Large Area Low Gray Level Emphasis probability = 1/(1 + e^-Rad_score)