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Authors
Xu, Yan
Lu, Lin https://orcid.org/0000-0001-7743-7022
Sun, Shawn H.
E, Lin-ning
Lian, Wei
Yang, Hao
Schwartz, Lawrence H.
Yang, Zheng-han
Zhao, Binsheng
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Funding
Funding for this research was provided by:
Natural Science Foundation of Beijing Municipality (7182040)
Division of Cancer Epidemiology and Genetics, National Cancer Institute (U01 CA225431)
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Article History
Received: 11 April 2021
Revised: 21 July 2021
Accepted: 16 August 2021
First Online: 21 September 2021
Declarations
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: The scientific guarantor of this publication is Dr. Binsheng Zhao.
: 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.
: Several authors have significant statistical and machine learning expertise.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained. The protocols in this study were approved by the Institutional Review Board of Beijing Friendship Hospital, Capital Medical University (Beijing, China) (2018-P2-100-01) and waived the requirement for informed consent because we retrospectively collected the patient data.
: The content of this manuscript has been presented in part at the IASLC 2019 World Conference on Lung Cancer, Xu Y, Lu L, Lian W, Schwartz L, Yang Z, Zhao B. P2.11-30 Effects of the size of nodules, reconstruction slice thickness and convolution kernel on radiomics model in classifying pulmonary nodules. <i>J Thorac Oncol</i>. 2019;14(10, Supplement): S804-S805. . Pars of the data have been used in the study published in AJR as “Xu Y, Lu L, E LN, et al Application of radiomics in predicting the malignancy of pulmonary nodules in different sizes. <i>AJR Am J Roentgenol.</i> 2019;213(6):1213-1220. doi:10.2214/AJR.19.21490.”
: • Retrospective.• case-controlled study.•performed at one institution.
