Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Abstract

According to the American Cancer Society it is estimated that around 2 million new cancer cases will be diagnosed in 2023 in the United States 1 . Medical imaging in oncology is the reference to evaluate most cancers, in particular for lesion detection and staging, which proves the need for general standards and guidelines in radiology to advance research in digital diagnosis. Medical images in radiomics play a key role not only in diagnosis, but also in monitoring the progression and development of tumors, in addition to supervising the response to therapy and risk of relapse 2 , 3 . Throughout the present text, the term radiomics will be used to encompass both classic radiomics and advanced data analysis techniques based on Artificial Intelligence (AI), such as deep radiomics 4 , 5 .