Radiomics has been used widely in image-based diagnosis and prognosis. Since radiomics studies often involve a small number of samples, effective dimension reduction and feature selection are crucial to the successful modeling. In this study, we proposed a heuristic method for effective dimension reduction and feature selection, which built a scout model for each category of features to select features from the category for the final model building. The approach was applied to the modeling with two different datasets, including the BraTS 2019 open data, and achieved results better than those of traditional methods on both datasets.
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