Wu, Kai
Wu, Peng
Yang, Kai
Li, Zhe
Kong, Sijia
Yu, Lu
Zhang, Enpu
Liu, Hanlin
Guo, Qing
Wu, Song http://orcid.org/0000-0003-3504-1630
Funding for this research was provided by:
the national natural science foundation fund of china (61931024, 81922046)
the national key research and development program of china (2017YFA0105900)
the special funds for strategic emerging industries development in shenzhen (20180309163446298)
shenzhen key laboratory program (ZDSYS20190902092857146)
shenzhen research institute of big data (2019ORF01007)
Article History
Received: 22 June 2021
Revised: 16 September 2021
Accepted: 22 September 2021
First Online: 20 November 2021
Declarations
:
: The scientific guarantor of this publication is Pro. Song Wu, who is the Head of the Institute of Urology, Shenzhen University, China. Email address: wusong@szu.edu.cn
: 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.
: Kai Wu and Peng Wu, two of the authors, majored in statistics and computer sciences, have significant statistical expertise.
: Informed consent documents are waived for all the participants are from an open-source project.
: This study was approved by the institutional research ethics committee. All data used were acquired with institutional review board-approved protocols.
: All the cohorts have been previously reported in <i>The Cancer Imaging Archive (TCIA)</i>, which is an open-source database and hosts a large archive of medical images accessible for public download. As far as we know, seldom researches integrated and analyzed all the kidney-cancer cohorts provided by <i>TCIA</i>. In this study, we proposed an analytical procedure by using these CT images, and firstly reported the correlation and causality between CT texture features and genomics in kidney cancer.
: • retrospective• diagnostic or prognostic study / observational• multicenter study