There are several important challenges in radiomics research; one of them is feature selection. Since many quantitative features are non-informative, feature selection becomes essential. Feature selection methods have been mixed with filter, wrapper, and embedded methods without a rule of thumb. This study aims to develop a framework for optimal feature selection in radiomics research. We developed the framework that the optimal features were selected to quickly through controlling relevance and redundancy among features. A 'FeatureMap' was generated containing information for each step and used as a platform. Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. We assessed the framework using four real datasets. The FeatureMap generated 6 combinations, with the number of features selected varying for each combination. The predictive models obtained high performances; the highest test area under the curves (AUCs) were 0.792, 0.820, 0.846 and 0.738 in the cross-validation method, respectively. We developed a flexible framework for feature selection methods in radiomics research and assessed its usefulness using various real-world data. Our framework can assist clinicians in efficiently developing predictive models based on radiomics.
Keywords: Feature selection; FeatureMap; Machine learning; Prediction model; Radiomics.
© 2024. The Author(s).