TY - JOUR
T1 - Radiomics in Lung Diseases Imaging
T2 - State-of-the-Art for Clinicians
AU - Frix, Anne-Noelle
AU - Cousin, Francois
AU - Refaee, Turkey
AU - Bottari, Fabio
AU - Vaidyanathan, Akshayaa
AU - Desir, Colin
AU - Vos, Wim
AU - Walsh, Sean
AU - Occhipinti, Mariaelena
AU - Lovinfosse, Pierre
AU - Leijenaar, Ralph T. H.
AU - Hustinx, Roland
AU - Meunier, Paul
AU - Louis, Renaud
AU - Lambin, Philippe
AU - Guiot, Julien
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - radiomics
KW - artificial intelligence
KW - lung diseases
KW - precision medicine
KW - IDIOPATHIC PULMONARY-FIBROSIS
KW - OBJECTIVE QUANTIFICATION
KW - AUTOMATED QUANTIFICATION
KW - MACROSCOPIC MORPHOMETRY
KW - VOLUME REDUCTION
KW - GROWTH-RATE
KW - CT
KW - NODULES
KW - EMPHYSEMA
KW - FEATURES
U2 - 10.3390/jpm11070602
DO - 10.3390/jpm11070602
M3 - (Systematic) Review article
C2 - 34202096
VL - 11
JO - Journal of Personalized Medicine
JF - Journal of Personalized Medicine
SN - 2075-4426
IS - 7
M1 - 602
ER -