Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians
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ID: 268229
2021
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Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.
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frix2021journalradiomics
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| Authors | Anne-Noëlle Frix;François Cousin;Turkey Refaee;Fabio Bottari;Akshayaa Vaidyanathan;Colin Desir;Wim Vos;Sean Walsh;Mariaelena Occhipinti;Pierre Lovinfosse;Ralph T. H. Leijenaar;Roland Hustinx;Paul Meunier;Renaud Louis;Philippe Lambin;Julien Guiot;Frix, Anne-Noëlle;Cousin, François;Refaee, Turkey;Bottari, Fabio;Vaidyanathan, Akshayaa;Desir, Colin;Vos, Wim;Walsh, Sean;Occhipinti, Mariaelena;Lovinfosse, Pierre;Leijenaar, Ralph T. H.;Hustinx, Roland;Meunier, Paul;Louis, Renaud;Lambin, Philippe;Guiot, Julien; |
| Journal | Journal of personalized medicine |
| Year | 2021 |
| DOI |
10.3390/jpm11070602
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| URL | |
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