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Authors
Lee, Taehee https://orcid.org/0000-0002-0592-8268
Ahn, Su Yeon
Kim, Jihang
Park, Jong Sun
Kwon, Byoung Soo
Choi, Sun Mi
Goo, Jin Mo
Park, Chang Min
Nam, Ju Gang https://orcid.org/0000-0003-3991-4523
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Funding
Funding for this research was provided by:
Ministry of Education, Science and Technology (2022R1A2C1091805)
Seoul National University Hospital (04-2022-0410)
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Article History
Received: 6 October 2023
Revised: 13 November 2023
Accepted: 15 November 2023
First Online: 19 December 2023
Declarations
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: The scientific guarantor of this publication is Ju Gang Nam.
: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: One of the authors (Ju Gang Nam) has significant statistical expertise.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: Some patients in our study (overlap, 94/1811) have been reported in previous publications on the validation of GAP index and the CT quantification of fibrosis on IPF. Neither of the studies focus on developing a prognostic model using chest radiographs.Kim ES, Choi SM, Lee J, et al (2015). Validation of the GAP score in Korean patients with idiopathic pulmonary fibrosis. <i>Chest</i> 147:430–437. ; Nam JG, Choi Y, Lee SM, et al (2023). Prognostic value of deep learning-based fibrosis quantification on chest CT in idiopathic pulmonary fibrosis. <i>Eur Radiol</i> 33:3144–3155. .
: • retrospective• diagnostic or prognostic study• multicenter study
