PT  - JOURNAL ARTICLE
AU  - Mathieu Hatt
AU  - Catherine Cheze Le Rest
AU  - Florent Tixier
AU  - Bogdan Badic
AU  - Ulrike Schick
AU  - Dimitris Visvikis
TI  - Radiomics: Data Are Also Images
AID  - 10.2967/jnumed.118.220582
DP  - 2019 Sep 01
TA  - Journal of Nuclear Medicine
PG  - 38S--44S
VI  - 60
IP  - Supplement 2
4099  - http://jnm.snmjournals.org/content/60/Supplement_2/38S.short
4100  - http://jnm.snmjournals.org/content/60/Supplement_2/38S.full
SO  - J Nucl Med2019 Sep 01; 60
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.