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

by Cousin, François , Meunier, Paul , Desir, Colin , Leijenaar, Ralph , Refaee, Turkey , Walsh, Sean , Occhipinti, Mariaelena , Guiot, Julien , Hustinx, Roland , Bottari, Fabio , Vaidyanathan, Akshayaa , Lambin, Philippe , Lovinfosse, Pierre , Frix, Anne-Noëlle , Vos, Wim , Louis, Renaud

in Accuracy / Algorithms / Artificial intelligence / Biomarkers / Biopsy / Cardiovascular & respiratory systems / Clinical outcomes / Computed tomography / Deep learning / Gene expression / Histology / Human health sciences / Lung cancer / Lung diseases / Magnetic resonance imaging / Medical prognosis / Medical screening / Medicine (miscellaneous) / Precision medicine / Prediction models / Pulmonary fibrosis / Radiologie, médecine & imagerie nucléaire / Radiology, nuclear medicine & imaging / Radiomics / Review / Sciences de la santé humaine / Software / Systèmes cardiovasculaire & respiratoire / Tumors

2021

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2021

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

2021

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Journal Article

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

Cousin, François,

Meunier, Paul,

Desir, Colin,

Leijenaar, Ralph,

Refaee, Turkey,

Walsh, Sean,

Occhipinti, Mariaelena,

Guiot, Julien,

Hustinx, Roland,

Bottari, Fabio,

Vaidyanathan, Akshayaa,

Lambin, Philippe,

Lovinfosse, Pierre,

Frix, Anne-Noëlle,

Vos, Wim,

Louis, Renaud

2021

Overview

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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Publisher

MDPI AG,MDPI

Subject

Accuracy

/ Algorithms

/ Artificial intelligence

/ Biomarkers

/ Biopsy

/ Cardiovascular & respiratory systems

/ Clinical outcomes