RT Journal Article SR Electronic T1 Radiomics: Data Are Also Images JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP 38S OP 44S DO 10.2967/jnumed.118.220582 VO 60 IS Supplement 2 A1 Mathieu Hatt A1 Catherine Cheze Le Rest A1 Florent Tixier A1 Bogdan Badic A1 Ulrike Schick A1 Dimitris Visvikis YR 2019 UL http://jnm.snmjournals.org/content/60/Supplement_2/38S.abstract AB 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.