Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.
artificial intelligence
lung diseases
precision medicine
radiomics
Journal
Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269
Informations de publication
Date de publication:
25 Jun 2021
25 Jun 2021
Historique:
received:
04
05
2021
revised:
16
06
2021
accepted:
21
06
2021
entrez:
2
7
2021
pubmed:
3
7
2021
medline:
3
7
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34202096
pii: jpm11070602
doi: 10.3390/jpm11070602
pmc: PMC8306026
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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