How to Extract Radiomic Features from Imaging

Radiomics analysis has been widely applied in cancer research and has demonstrated its potential to improve patient care. The process of radiomics analysis involves several steps, starting with image acquisition and preprocessing, followed by segmentation. Radiomics features are then extracted, which include shape, intensity, texture, and statistical measures, among others. These features are then subjected to machine learning algorithms to identify patterns and relationships between features and clinical endpoints. In addition to radiomics features, other features such as deep features or other imaging biomarkers can be extracted from the image. Standardization emerges as a crucial aspect in radiomics analysis, ensuring consistency and reproducibility. The Imaging Biomarkers Standardization Initiative (IBSI) provides guidelines for radiomics feature extraction. Deep learning models have emerged as a promising alternative to feature-based models. These models learn features automatically, which can help overcome the limitations of feature-based models that are sensitive to inter-scanner and inter-protocol variability. Radiomics is a rapidly growing field that has the potential to transform medical imaging and improve patient outcomes. By providing quantitative information on tissue structure and function, radiomics analysis can help clinicians make more informed treatment decisions and develop personalized treatment strategies.