Advancements in radiomics: A comprehensive survey of feature types and their correlation on modalities and regions

This survey paper provides an overview of different feature types used in radiomics research and their applications across various medical imaging modalities and disease domains. The paper delves into the key aspects of the radiomics workflow, including data engineering techniques for image acquisition, preprocessing, fusion, and segmentation. It then presents a comprehensive review of the most commonly employed feature categories in radiomics, such as shape-based, first-order statistical, second-order texture, and transform-based features. The paper also discusses the emerging role of deep learning features extracted using convolutional neural networks, recurrent neural networks, and transformers. The analysis of feature usage trends across different anatomical regions and imaging modalities offers valuable insights that can guide the optimization of feature engineering strategies in future radiomics research. The survey concludes by highlighting several opportunities for further advancement in the field, including the need for larger multi-center datasets, multi-modal data fusion, self-supervised learning, and the development of efficient embedded models for on-device deployment.