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Abdominal Radiology
Erschienen in: Abdominal Radiology 9/2022

25.08.2021 | Special Section: Quantitative Imaging

Radiomics: a primer on high-throughput image phenotyping

verfasst von: Kyle J. Lafata, Yuqi Wang, Brandon Konkel, Fang-Fang Yin, Mustafa R. Bashir

Erschienen in: Abdominal Radiology | Ausgabe 9/2022

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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.

Graphic abstract

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Metadaten
Titel
Radiomics: a primer on high-throughput image phenotyping
verfasst von
Kyle J. Lafata
Yuqi Wang
Brandon Konkel
Fang-Fang Yin
Mustafa R. Bashir
Publikationsdatum
25.08.2021
Verlag
Springer US
Erschienen in
Abdominal Radiology / Ausgabe 9/2022
Print ISSN: 2366-004X
Elektronische ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-021-03254-x

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