Radiomics: Data Are Also Images

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.

• Publication year

  2019

• Venue

  Journal of Nuclear Medicine

• Publication date

  2019-09-01

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.2967/jnumed.118.220582PMID 31481588
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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