Fig. 1: Feature selection for radiomics.

In this illustration, a model classifier is shown to differentiate benign from malignant breast lesions on imaging. Initially, a large number of radiomic features were computed and after removing the highly correlated features, the zero and near-zero variance features; a recursive feature elimination and reduction method was applied. The model performance illustrated here identifies11 features to be at the saturation point. The red curve (left) is showing accuracy versus number of features, while the blue curve (right) represents the model’s error function over the number of features. In this example, using 11 imaging features shows high accuracy while minimising the error function.