The Emerging Role of Radiomics in COPD and Lung Cancer

Turkey Refaee; Guangyao Wu; Abdallah Ibrahim; Iva Halilaj; Ralph T. H. Leijenaar; William Rogers; Hester A. Gietema; Lizza E. L. Hendriks; Philippe Lambin; Henry C. Woodruff

doi:10.1159/000505429

The Emerging Role of Radiomics in COPD and Lung Cancer

Turkey Refaee^*, Guangyao Wu, Abdallah Ibrahim, Iva Halilaj, Ralph T. H. Leijenaar, William Rogers, Hester A. Gietema, Lizza E. L. Hendriks, Philippe Lambin, Henry C. Woodruff

^*Corresponding author for this work

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

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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. (C) 2020 S. Karger AG, Basel

Original language	English
Pages (from-to)	99-107
Number of pages	9
Journal	Respiration
Volume	99
Issue number	2
DOIs	https://doi.org/10.1159/000505429
Publication status	Published - 2020

Keywords

Radiomics
Chronic obstructive pulmonary disease
Lung cancer
QUANTITATIVE COMPUTED-TOMOGRAPHY
OBSTRUCTIVE PULMONARY-DISEASE
FACTOR RECEPTOR MUTATION
OBJECTIVE QUANTIFICATION
MACROSCOPIC MORPHOMETRY
CENTRILOBULAR EMPHYSEMA
PROGNOSTIC VALUE
GLOBAL STRATEGY
DENSITY MASK
CT

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title = "The Emerging Role of Radiomics in COPD and Lung Cancer",

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. (C) 2020 S. Karger AG, Basel",

keywords = "Radiomics, Chronic obstructive pulmonary disease, Lung cancer, QUANTITATIVE COMPUTED-TOMOGRAPHY, OBSTRUCTIVE PULMONARY-DISEASE, FACTOR RECEPTOR MUTATION, OBJECTIVE QUANTIFICATION, MACROSCOPIC MORPHOMETRY, CENTRILOBULAR EMPHYSEMA, PROGNOSTIC VALUE, GLOBAL STRATEGY, DENSITY MASK, CT",

author = "Turkey Refaee and Guangyao Wu and Abdallah Ibrahim and Iva Halilaj and Leijenaar, {Ralph T. H.} and William Rogers and Gietema, {Hester A.} and Hendriks, {Lizza E. L.} and Philippe Lambin and Woodruff, {Henry C.}",

note = "Funding Information: The authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, No. 694812 – Hypoximmuno) and ERC-2018-PoC (No. 81320-CL-IO). This research is also supported by the Dutch Technology Foundation STW (grant No. P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. The authors also acknowledge financial support from SME Phase 2 (RAIL – No. 673780), EUROSTARS (DART, DECIDE), PREDICT (ITN – No. 766276), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY” – No. UM 2017-8295), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”), Kan-keronderzoekfonds Limburg (KOFL) from the Health Foundation Limburg, and the Dutch Cancer Society. Publisher Copyright: {\textcopyright} 2020 S. Karger AG, Basel.",

year = "2020",

doi = "10.1159/000505429",

language = "English",

volume = "99",

pages = "99--107",

journal = "Respiration",

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AU - Refaee, Turkey

AU - Wu, Guangyao

AU - Ibrahim, Abdallah

AU - Halilaj, Iva

AU - Leijenaar, Ralph T. H.

AU - Rogers, William

AU - Gietema, Hester A.

AU - Hendriks, Lizza E. L.

AU - Lambin, Philippe

AU - Woodruff, Henry C.

N1 - Funding Information: The authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, No. 694812 – Hypoximmuno) and ERC-2018-PoC (No. 81320-CL-IO). This research is also supported by the Dutch Technology Foundation STW (grant No. P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. The authors also acknowledge financial support from SME Phase 2 (RAIL – No. 673780), EUROSTARS (DART, DECIDE), PREDICT (ITN – No. 766276), TRANSCAN Joint Transnational Call 2016 (JTC2016 “CLEARLY” – No. UM 2017-8295), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”), Kan-keronderzoekfonds Limburg (KOFL) from the Health Foundation Limburg, and the Dutch Cancer Society. Publisher Copyright: © 2020 S. Karger AG, Basel.

PY - 2020

Y1 - 2020

N2 - 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. (C) 2020 S. Karger AG, Basel

AB - 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. (C) 2020 S. Karger AG, Basel

KW - Radiomics

KW - Chronic obstructive pulmonary disease

KW - Lung cancer

KW - QUANTITATIVE COMPUTED-TOMOGRAPHY

KW - OBSTRUCTIVE PULMONARY-DISEASE

KW - FACTOR RECEPTOR MUTATION

KW - OBJECTIVE QUANTIFICATION

KW - MACROSCOPIC MORPHOMETRY

KW - CENTRILOBULAR EMPHYSEMA

KW - PROGNOSTIC VALUE

KW - GLOBAL STRATEGY

KW - DENSITY MASK

KW - CT

U2 - 10.1159/000505429

DO - 10.1159/000505429

M3 - (Systematic) Review article

C2 - 31991420

SN - 0025-7931

VL - 99

SP - 99

EP - 107

JO - Respiration

JF - Respiration

IS - 2

ER -

The Emerging Role of Radiomics in COPD and Lung Cancer

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Keywords

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