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Review
. 2020;99(2):99-107.
doi: 10.1159/000505429. Epub 2020 Jan 28.

The Emerging Role of Radiomics in COPD and Lung Cancer

Affiliations
Review

The Emerging Role of Radiomics in COPD and Lung Cancer

Turkey Refaee et al. Respiration. 2020.

Abstract

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.

Keywords: Chronic obstructive pulmonary disease; Lung cancer; Radiomics.

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Conflict of interest statement

Dr. Philippe Lambin reports, within and outside the submitted work, grants/sponsored research agreements from Varian medical, Oncoradiomics, ptTheragnostic and Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursement of travel costs/external grant writing fee and/or in kind manpower contribution from Oncoradiomics, BHV, Merck, Varian, Elekta and Convert pharmaceuticals. Dr. Lambin has (minority) shares in the company Oncoradiomics SA and Convert pharmaceuticals SA and is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Oncoradiomics and one issue patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito, three nonpatentable invention (softwares) licensed to ptTheragnostic/DNAmito, Oncoradiomics and Health Innovation Ventures. Dr. Woodruff and Dr. Leijenaar have (minority) shares in the company Oncoradiomics.

Figures

Fig. 1
Fig. 1
Different distributions of HU values extracted from the ROI (purple outline) for normal tissue (a), COPD tissue (b), and lung tumor (c).
Fig. 2
Fig. 2
Graphic depiction of the radiomics workflow.

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