Liu, Zhaobang
Li, Ming
Zuo, Changjing
Yang, Zehong
Yang, Xiaokai
Ren, Shengnan
Peng, Ye
Sun, Gaofeng
Shen, Jun
Cheng, Chao
Yang, Xiaodong http://orcid.org/0000-0002-5828-307X
Funding for this research was provided by:
National Natural Science Foundation of China (61701492)
National Key Research and Development Program of China (2016YFC0103502)
Wenzhou Science and Technology Foundation (ZS2017020)
Medical artificial intelligence project of Sun Yat-Sen Memorial Hospital (YXRGZN201905)
Article History
Received: 1 October 2020
Revised: 19 January 2021
Accepted: 11 February 2021
First Online: 6 March 2021
Compliance with ethical standards
:
: The scientific guarantor of this publication is Prof. Xiaodong Yang.
: 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 has significant statistical expertise.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: Some study subjects or cohorts have been previously reported in other journals.The previous work published in the journal <i>Medical Physics</i> was named “Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT.” Early work only focused on comparisons of feature selection strategies and classifiers using data from PET/CT single-time imaging. The number of patients was 45 AIP and 66 PDAC, which partially overlaps with our current case. Our current work is mainly to study the advantages of PET/CT dual-time imaging in distinguishing AIP and PDAC via radiomics analysis. Our number of patients is 48 AIP cases and 64 PDAC cases.
: • retrospective• diagnostic or prognostic study• performed at one institution