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Figure 3: Radiomics feature selection using the least absolute shrinkage and selection operator regression algorithm. (a) Least absolute shrinkage and selection operator coefficient profiles of the 13 radiomics features. A coefficient profile plot was generated versus the selected-log (λ) value. A vertical line was plotted at the optimal λ value. Each color line represents the change track of each feature coefficient. (b) The parameter (λ) selection in the least absolute shrinkage and selection operator model used 5-fold cross-validation. The y-axis indicates the mean square error. The x-axis indicates the log (λ). The black curve indicates the average error for each model with a given λ. Each color line represents the error for each model with a given λ. The vertical lines define the optimal λ value of 0.04 with log (λ) =1.41

Figure 3: Radiomics feature selection using the least absolute shrinkage and selection operator regression algorithm. (a) Least absolute shrinkage and selection operator coefficient profiles of the 13 radiomics features. A coefficient profile plot was generated versus the selected-log (λ) value. A vertical line was plotted at the optimal λ value. Each color line represents the change track of each feature coefficient. (b) The parameter (λ) selection in the least absolute shrinkage and selection operator model used 5-fold cross-validation. The y-axis indicates the mean square error. The x-axis indicates the log (λ). The black curve indicates the average error for each model with a given λ. Each color line represents the error for each model with a given λ. The vertical lines define the optimal λ value of 0.04 with log (λ) =1.41