Medical imaging has undergone a transformative shift with the integration of Artificial Intelligence (AI), particularly through the emerging fields of Radiomics and Radio genomics. Cancer and other complex diseases demand early, accurate, and non-invasive diagnostic approaches that go beyond the limitations of conventional histopathological and visual interpretation methods. Radiomics enables the extraction of high-dimensional quantitative features — including shape, intensity, texture, and spatial relationships — from standard medical images such as CT, MRI, PET, and ultrasound, converting them into actionable mineable data. Radiogenomics further extends this framework by correlating these imaging phenotypes with genomic and molecular alterations, offering a deeper, non-invasive understanding of disease biology. AI algorithms, particularly Machine Learning (ML) and Deep Learning (DL) models such as Convolutional Neural Networks (CNNs), have demonstrated high accuracy in tumour detection, segmentation, classification, and treatment response prediction. Together, these technologies hold significant promise in oncology for differentiating benign from malignant tumours, predicting patient survival, and guiding personalised therapeutic strategies. However, challenges, including a lack of standardisation, limited high-quality datasets, and data privacy concerns, must be addressed for broader clinical adoption. This review evaluates the current role, clinical applications, and limitations of AI-based Radiomics and Radiogenomics in advancing precision medicine and improving patient outcomes.
Pragati et al. (Mon,) studied this question.
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