Nuclear medicine radiomics in precision medicine: why we can't do without artificial intelligence

Willemke Antonia Noortman*, Dennis Vriens, Willem Grootjans, Qian Tao, Lioe-Fee de Geus-Oei, Floris H.P. van Velden

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

8 Citations (Scopus)


In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction, thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose, at the right time.
Original languageEnglish
Pages (from-to)278-290
Number of pages13
JournalThe Quarterly journal of nuclear medicine and molecular imaging
Issue number3
Publication statusPublished - Sept 2020


  • Nuclear medicine
  • Diagnostic imaging
  • Radiography
  • Artificial intelligence
  • n/a OA procedure


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