Radiomics: more than meets the eye
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We are living a medical image revolution. Medical imaging is an essential component of our healthcare system, as it is used for screening, early diagnosis, treatment selection, and follow-up. Medical imaging is responsible for a substantial increase in medical research data and is undergoing a fundamental transformation due to the growth of machine learning. Advances in medical image processing techniques contributed to make a fundamental change in the approach of interpreting medical images: from visual interpretation to quantitative analysis.
Radiomics is a class of advanced imaging techniques that allow non-invasive, high-throughput extraction of a large numbers of quantitative features from medical images. The use of Radiomics, in cancer research in particular, is of great interest and the number of publications using these techniques are rising rapidly. Radiomics analysis is not limited to conventional radiological imaging modalities such as Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, and Ultrasound but can also be applied to other forms of images such as Mammography and Pathology. In cancer, variations in image features of the primary tumour are thought to reflect underlying tumour biology, such as areas of hypoxia, necrosis, angiogenesis and proliferation. Associating Radiomics data with biological and clinical endpoints could enhance the diagnosis, staging, treatment planning and monitoring of patients.
In this talk we will describe the process of radiomics, its challenges, and its power to combine image and wider datasets to build validated models that have the potential to help making better clinical decision for a more personalised approach to diagnosis and care.