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Radiomics: a primer on high-throughput image phenotyping

  • Special Section: Quantitative Imaging
  • Published:
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Abstract

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology’s special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.

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Authors and Affiliations

  1. Department of Radiology, Duke University School of Medicine, Durham, NC, USA

    Kyle J. Lafata, Brandon Konkel & Mustafa R. Bashir

  2. Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA

    Kyle J. Lafata & Fang-Fang Yin

  3. Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA

    Kyle J. Lafata & Yuqi Wang

  4. Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA

    Mustafa R. Bashir

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  1. Kyle J. Lafata
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Lafata, K.J., Wang, Y., Konkel, B. et al. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol 47, 2986–3002 (2022). https://doi.org/10.1007/s00261-021-03254-x

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