Zhang, Gumuyang
Wu, Zhe
Zhang, Xiaoxiao
Xu, Lili
Mao, Li
Li, Xiuli
Xiao, Yu
Ji, Zhigang
Sun, Hao http://orcid.org/0000-0002-9606-9066
Jin, Zhengyu
Funding for this research was provided by:
National Natural Science Foundation of China (81901742, 91859119)
Natural Science Foundation of Beijing Municipality (7192176)
clinical and translational research project of chinese academy of medical sciences (XK320028)
national public welfare basic scientific research project of chinese academy of medical sciences (2018PT32003, 2019PT320008)
Article History
Received: 28 August 2021
Revised: 12 October 2021
Accepted: 18 October 2021
First Online: 22 January 2022
Declarations
:
: The scientific guarantor of this publication is Hao Sun.
: The authors of this manuscript declare relationships with the following companies: Li Mao and Xiuli Li are employees of Deepwise AI Lab, Deepwise Inc., which contributed to the development of radiomics models described in the study. All remaining authors have declared no conflicts of interest.
: 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 have been previously reported in the study of “CT-based radiomics to predict the pathological grade of bladder cancer” published in European Radiology (doi: ExternalRef removed), and the study entitled “Deep learning on enhanced CT images can predict the muscular invasiveness of bladder cancer” published in Frontiers in oncology (doi: ExternalRef removed). The article published in <i>European Radiology</i> focused on pathological grade of bladder cancer while the article published in Frontiers in oncology applied deep learning technique rather than radiomics. Both studies were different from the present study.
: • retrospective• diagnostic or prognostic study• performed at multicenter