Radiomics and radiogenomics with artificial intelligence: Approaches, applications, advances, current challenges, and future perspectives

Abstract

The focus of this chapter is to propose advancements in magnetic resonance imaging, positron emission tomography, and computed tomography (CT) radiomics research and to present predictive models intending to provide personalized clinical management. Radiomics provides statistical and computational methods to extract huge numbers of quantitative features from medical imaging modalities. Also, the advantages and disadvantages of the research are examined to determine challenges for providing meaningful future prospects. The research includes clinical and technical objectives. They involve diagnosis, prediction, staging, and outcome prediction. The radiomics and radiogenomics main steps that are relevant to the clinical and technical issues are examined and future prospects are discussed. Also, advances in artificial intelligence related to medical imaging present medical images as valuable data sources. They allow the conversion of digital medical images into high-dimensional data suitable for data science and data mining. Furthermore, the given data source can be used for the improvement of clinical decision support systems. Meanwhile, radiomics and deep learning have made enormous progress and they are the most remarkable quantitative imaging techniques.