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

Anne-Noelle Frix, Francois 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*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

8 Citations (Web of Science)

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.

Original languageEnglish
Article number602
Number of pages20
JournalJournal of Personalized Medicine
Volume11
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

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

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