Statistical analysis
RS
REGRESSION
Statistical analysis was conducted using R software (version 3.6.1, Institute for Statistics and Mathematics, Vienna, Austria) and SPSS statistical software (version 21.0, SPSS Inc, IBM, Armonk, NY, U.S.A.). The Chi-square test and independent samples t-test were used to investigate the differences between categorical variables and continuous variables. The correlations between texture variables were assessed using Pearson’s correlation coefficient with the R package “psych”. The LASSO was applied for feature selection, and RS was calculated by summing the selected features weighted by their β-coefficients, where RS = 3.37022285 * GLZLM_LZLGE + -0.04245328 * GLZLM_LGZE + 1.25470569 * Energy. The cut-off value of RS was determined according to the Youden’s J statistics (J = sensitivity + specificity − 1). Univariate analysis and multivariate logistic regression analysis were applied to investigate independent predictive factors. A nomogram was developed using the R package “rms” to predict the treatment effect, and the discrimination power of the nomogram was evaluated by calculating the C-index. In addition, calibration plot was generated using bootstrapping with 1000 resamples. To evaluate the clinical utility of the nomogram, DCA was performed by quantifying the net benefits at different threshold probabilities, and the clinical impact curve was performed by quantifying the number of high risk at each threshold probability.