A review in radiomics: Making personalized medicine a reality via routine imaging.
artificial intelligence
deep learning
machine learning
personalized medicine
radiomics
Journal
Medicinal research reviews
ISSN: 1098-1128
Titre abrégé: Med Res Rev
Pays: United States
ID NLM: 8103150
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
revised:
05
07
2021
received:
18
01
2021
accepted:
07
07
2021
pubmed:
27
7
2021
medline:
5
4
2022
entrez:
26
7
2021
Statut:
ppublish
Résumé
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
426-440Informations de copyright
© 2021 Wiley Periodicals LLC.
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