Radiomics is field of study in Medicine emerging from the merger of data science and medical imaging. It first arose as application in Oncology however, it is applicable in any branch of medicine where tomographic imaging is in use to detect, diagnose and make treatment decisions for any illness or condition. Type of tomography is also irrelevant for the Radiomics. It could be MRI, CT,... anything that produces a three dimensional scan capturing disease identifying characteristics. Any additional set of data parameters that is relevant to the diagnosis (ex. clinical, genomics or any other set) can be integrated in the process with the image as all of it is seen as an illness describing data set containing valuable decision making information.
We can see in the illustration from the seminal paper about Radiomics by R.J.Gillies and others, Radiomics processing includes steps of data collection by imaging a group of patients and identifying disease markers and/or areas, 3D rendering and defining important identifying patterns, extracting crucial features and merging them with any other non-imaging data sets followed by the data science processing and the creation of models which than can be used for diagnosis and treatment decisions of new patients.
Every new research field has its challenges during early adoption and initial development. Radiomics shares most of them, underlined by the factor that it is dealing with the fundamental human well-being. Crucial cornerstones are reproducibility and Big Data issues. Minimal protocol, decision making or software choices may create seeming conflict of results from various institutions as the very object of study is extremely complex, creating opportunities for discrepancies. Big Data has its own complexity and volume issues which can further amplify any problem or misunderstanding within Radiomics research. Yet, this field promises results akin to a good Sci-Fi in our lifetime.