Bonekamp, David http://orcid.org/0000-0002-4811-0087
Schelb, Patrick
Wiesenfarth, Manuel
Kuder, Tristan Anselm
Deister, Fenja
Stenzinger, Albrecht
Nyarangi-Dix, Joanne
Röthke, Matthias
Hohenfellner, Markus
Schlemmer, Heinz-Peter
Radtke, Jan Philipp
Article History
Received: 19 May 2018
Revised: 15 August 2018
Accepted: 11 September 2018
First Online: 16 October 2018
Compliance with ethical standards
:
: The scientific guarantor of this publication is Heinz-Peter Schlemmer.
: David Bonekamp is a speaker for Profound Medical Inc.Patrick Schelb has nothing to declare.Manuel Wiesenfarth has nothing to declare.Tristan Anselm Kuder has nothing to declare.Fenja Deister has nothing to declare.Albrecht Stenzinger declares the following: consulting fee and payment for lectures: Astra Zeneca, BMS, Novartis, Roche, Illumina, Thermo Fisher; travel support: Astra Zeneca, BMS, Novartis, Illumina, Thermo Fisher; board member: Astra Zeneca, BMS, Novartis, Thermo Fisher.Joanne Nyarangi-Dix has nothing to declare.Matthias Röthke declares consulting fee and payment for lectures: Siemens Healthineers, Curagita AG.Markus Hohenfellner has nothing to declare.Heinz-Peter Schlemmer declares the following: consulting fee or honorarium: Siemens, Curagita, Profound, Bayer; travel support: Siemens, Curagita, Profound, Bayer; board member: Curagita; consultancy: Curagita, Bayer; grants/grants pending: BMBF, Deutsche Krebshilfe, Dietmar-Hopp-Stiftung, Roland-Ernst-Stiftung; payment for lectures: Siemens, Curagita, Profound, Bayer.Jan Philipp Radtke declares payment for consultant work from Saegeling Medizintechnik and Siemens Heathineers and for development of educational presentations from Saegeling Medizintechnik.
: Manuel Wiesenfarth is the lead statistician and co-author on this paper.
: Written informed consent was waived by the Ethics Commission.
: Ethical approval was obtained.
: The examined cohort was subject to a recently published study (Bonekamp D, Kohl S, Wiesenfarth M et al (2018) Radiomic machine learning for characterization of prostate lesions by MRI: comparison to ADC values. Radiology 31:173064. ExternalRef removed. Focusing on apparent diffusion coefficient and radiomics for lesion classification; however, sextant-level histopathology to mpMRI mapping has not been previously performed.
: • retrospective• diagnostic study• single-center study