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Authors
Du, Dongyang
Lv, Wenbing
Lv, Jieqin
Chen, Xiaohui
Wu, Hubing
Rahmim, Arman
Lu, Lijun https://orcid.org/0000-0002-7001-4892
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Funding
Funding for this research was provided by:
National Natural Science Foundation of China (81871437, 12026601)
Basic and Applied Basic Research Foundation of Guangdong Province (2019A1515011104, 2020A1515110683, 2021A1515011676)
Postdoctoral Research Foundation of China (2020M682792)
China Scholarship Council
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Article History
Received: 5 July 2022
Revised: 11 August 2022
Accepted: 9 October 2022
First Online: 10 November 2022
Declarations
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: The scientific guarantor of this publication is Dr. Lijun Lu.
: 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.
: No complex statistical methods were necessary for this paper.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: From January 2013 to March 2019, 164 of 174 patients reported by our previous study [] were enrolled to differentiate between lung cancer and active pulmonary tuberculosis. This prior article analyzed diagnostic performance of PET/CT radiomics features combined with semantic features, whereas our current study reports on the effect of reconstruction kernels on radiomics features and feasibility of inter-image conversion based on deep learning towards improved reproducibility of radiomics features and performance of radiomics models.
: • retrospective• experimental• performed at one institution
