Statistical analysis
Quantitative variables are reported as mean ± standard deviation and qualitative data as number and percentage. The survival curves were established by the Kaplan-Meier method. Multivariate analyses were performed to identify independent predictors of OS and EFS. To take into account the correlation between the estimates of each texture parameter from the different filter values as well as the small number of events compared with the number of included covariates, multivariate L1 (least absolute shrinkage and selection operator—Lasso) penalized Cox regression models logistic regression models were built in order to select clinical and texture parameters(7). The regularization parameter was determined by using fivefold cross-validation. The Lasso method allows variable selection by shrinking down to zero coefficient weights for variables non-related to outcome. Variables with non-zero coefficients were selected as potential predictors of outcome and integrated into multivariable Cox regression analyses in order to estimate associated hazard ratios (HR) and their 95% confidence intervals (CI 95%).
For each texture parameter associated with outcome in multivariate analysis, a receiver operating characteristic (ROC) curve was constructed to identify the most relevant threshold.
A p value < 0.05 was considered statistically significant. Analyses were performed using SAS version 9.4 (SAS Inc, Cary, NC, USA) and R version 3.6 (R Foundation for Statistical Computing, Vienna, Austria).