Rodriguez-Ruiz, Alejandro
Lång, Kristina
Gubern-Merida, Albert
Teuwen, Jonas
Broeders, Mireille
Gennaro, Gisella
Clauser, Paola
Helbich, Thomas H.
Chevalier, Margarita
Mertelmeier, Thomas
Wallis, Matthew G.
Andersson, Ingvar
Zackrisson, Sophia
Sechopoulos, Ioannis
Mann, Ritse M.
Funding for this research was provided by:
Radboud University Medical Center
Article History
Received: 13 January 2019
Revised: 12 March 2019
Accepted: 20 March 2019
First Online: 16 April 2019
Compliance with ethical standards
:
: The scientific guarantor of this publication is Ritse Mann.
: The authors of this manuscript declare relationships with the following companies:The authors KL, PC, TH, TM, SZ, IS, and RM of this manuscript declare relationships with Siemens Healthineers (Erlangen, Germany): TM is an employee, KL, PC, TH, SZ, IS, and RM received research grants.The authors AR, AG, and RM declare relationships with ScreenPoint Medical BV (Nijmegen, Netherlands): AR and AG are employees, RM is an advisor.
: Dr. Brandon Gallas, Dr. Weijie Chen, and Mr. Qi Gong (Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA, Silver Spring, MD, USA) kindly provided statistical advice for this manuscript.One of the authors has significant statistical expertise.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.
: Some study subjects or cohorts have been previously reported in (“Stand-alone artificial intelligence for breast cancer detection in mammography: Comparison with 101 radiologists” by A. Rodriguez-Ruiz et al 2018, accepted in December 2018, Journal of the National Cancer Institute).
: • retrospective• experimental• multicenter study