TY - JOUR T1 - Radiomics: Data Are Also Images JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 38S LP - 44S DO - 10.2967/jnumed.118.220582 VL - 60 IS - Supplement 2 AU - Mathieu Hatt AU - Catherine Cheze Le Rest AU - Florent Tixier AU - Bogdan Badic AU - Ulrike Schick AU - Dimitris Visvikis Y1 - 2019/09/01 UR - http://jnm.snmjournals.org/content/60/Supplement_2/38S.abstract N2 - The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y. ER -