Radiogenomics in Oncology

Abstract

The emergence of artificial intelligence in the digital era has brought about a significant transformation in the field of clinical decision support systems. The advent of technological advancements has led to the development of novel data-driven analytical algorithms, hence greatly augmenting human capacity to process information. The field of cancer radiogenomics presents a promising area within the realm of precision medicine. The objective of our research is to enhance our understanding of the genetic factors that contribute to the formation of tumors. This will be achieved by integrating extensive radiomics features extracted from medical imaging, genetic data obtained from clinical-epidemiological sources, and insights derived from high-throughput sequencing using mathematical modelling techniques. The aim of integrating radiomics and genomes is to gain a deeper understanding of the complex mechanisms behind cancer growth. The primary aim is to develop novel, empirically supported methodologies for the identification, prediction, and individualized therapeutic strategies for cancer, utilizing the acquired understanding. This comprehensive review aims to provide an overview of the existing body of research on the applications of radiogenomics, with a specific focus on solid malignancies. Additionally, we will examine the barriers that are now preventing the widespread integration of radiomics into therapeutic contexts.