Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians

被引：60

作者：

Frix, Anne-Noelle ^{[1
]}

Cousin, Francois ^{[2
]}

Refaee, Turkey ^{[3
,4
]}

Bottari, Fabio ^{[5
]}

Vaidyanathan, Akshayaa ^{[3
,5
]}

Desir, Colin ^{[6
]}

Vos, Wim ^{[5
]}

Walsh, Sean ^{[5
]}

Occhipinti, Mariaelena ^{[5
]}

Lovinfosse, Pierre ^{[2
]}

Leijenaar, Ralph T. H. ^{[5
]}

Hustinx, Roland ^{[2
]}

Meunier, Paul ^{[6
]}

Louis, Renaud ^{[1
]}

Lambin, Philippe ^{[3
]}

Guiot, Julien ^{[1
]}

机构：

[1] Univ Hosp Liege, Dept Resp Med, B-4000 Liege, Belgium

[2] Univ Hosp Liege, Dept Nucl Med & Oncol Imaging, B-4000 Liege, Belgium

[3] Maastricht Univ, GROW Sch Oncol, Dept Precis Med, D Lab, NL-6229 Maastricht, Netherlands

[4] Jazan Univ, Fac Appl Sci, Dept Diagnost Radiol, Jazan 45142, Saudi Arabia

[5] Radiomics, Res & Dev, B-4000 Liege, Belgium

[6] Univ Hosp Liege, Dept Radiol, B-4000 Liege, Belgium

来源：

JOURNAL OF PERSONALIZED MEDICINE | 2021年 / 11卷 / 07期

关键词：

radiomics; artificial intelligence; lung diseases; precision medicine; IDIOPATHIC PULMONARY-FIBROSIS; OBJECTIVE QUANTIFICATION; AUTOMATED QUANTIFICATION; MACROSCOPIC MORPHOMETRY; VOLUME REDUCTION; GROWTH-RATE; CT; NODULES; EMPHYSEMA; FEATURES;

D O I：

10.3390/jpm11070602

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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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