In Radiology ; h5-index 91.0
Radiomic analysis offers a powerful tool for the extraction of clinically relevant information from radiologic imaging. Radiomics can be used to predict patient outcome through automated high-throughput feature extraction, using large training cohorts to elucidate subtle relationships between image characteristics and disease status. However powerful, the data-driven nature of radiomics inherently offers no insight into the biological underpinnings of the observed relationships. Early radiomics work was dominated by analysis of semantic, radiologist-defined features and carried qualitative real-world meaning. Following the rapid developments and popularity of machine learning approaches, the field moved quickly toward high-throughput agnostic analyses, resulting in increasingly large feature sets. This trend took the focus toward an increase in predictive power and further away from a biological understanding of the findings. Such a disconnect between predictor model and biological meaning will inherently limit broad clinical translation. Efforts to reintroduce biological meaning into radiomics are gaining traction in the field with distinct emerging approaches available, including genomic correlates, local microscopic pathologic image textures, and macroscopic histopathologic marker expression. These methods are presented in this review, and their significance is discussed. The authors predict that following the increasing pressure for robust radiomics, biological validation will become a standard practice in the field, thus further cementing the role of the method in clinical decision making.
Tomaszewski Michal R, Gillies Robert J