Yang, Huancheng
Wu, Kai
Liu, Hanlin
Wu, Peng
Yuan, Yangguang
Wang, Lei
Liu, Yaru
Zeng, Haoyang
Li, Junkai
Liu, Weihao
Wu, Song http://orcid.org/0000-0003-3504-1630
Funding for this research was provided by:
Innovative Research Group Project of the National Natural Science Foundation of China (61931024)
Guangdong Basic and Applied Basic Research Foundation (2019A1515110038)
Shenzhen Fundamental Research Program (202208183000146)
Special Funds for the Basic Research and Development Program in the Central Non-profit Research Institutesof China (20180309163446298)
Shenzhen Science and Technology Innovation Program (RCJC20200714114557005)
Article History
Received: 9 February 2023
Revised: 14 March 2023
Accepted: 27 March 2023
First Online: 8 June 2023
Declarations
:
: The scientific guarantor of this publication is Prof. Song Wu.
: 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 majored in statistics and computer sciences, and has significant statistical expertise.
: Informed consent documents are waived by the Institutional Review Board.
: 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 The Cancer Imaging Archive (TCIA), 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 TCIA. In this study, we proposed an analytical procedure by using these CT images, and firstly reported the automated surgical decision framework for partial or radical nephrectomy based on 3D CT multi-level anatomical features in renal cell carcinoma.
: <b>• </b>retrospective<b>• </b>surgical decision-making study/observational<b>• </b>multicentre study