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Authors
Fransen, Stefan J. https://orcid.org/0000-0002-5454-5052
Bosma, Joeran S.
van Lohuizen, Quintin
Roest, Christian
Simonis, Frank F. J.
Kwee, Thomas C.
Yakar, Derya
Huisman, Henkjan
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Funding
Funding for this research was provided by:
Health~Holland and Siemens Healthineers (LSHM20103)
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Article History
Received: 22 October 2024
Revised: 22 October 2024
Accepted: 30 April 2025
First Online: 7 June 2025
Compliance with ethical standards
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: The scientific guarantor of this publication is S.J. Fransen.
: D.Y. is a member of the Scientific Editorial Board of European Radiology (section: Imaging Informatics and Artificial Intelligence). As such, they have not participated in the selection nor review processes for this article. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: No complex statistical methods were necessary for this paper.
: Written informed consent was waived by the Institutional Review Boards (center A: METc-2018/597, center B: MEC-2019-056, and center C: 2017/576).
: Institutional Review Board approval was obtained (center A: METc-2018/597, center B: MEC-2019-056, and center C: 2017/576).
: Some study subjects or cohorts have been previously reported in previous studies, but with different purposes: deep learning prostate cancer detection (1–4), radiomics prostate cancer detection (5), csPCa biopsy detection efficacy (6), and prostate cancer follow-up detection (7). Our study uses a state-of-the-art AI system to investigate the potential for radiologists’ workload reduction in a semi-autonomous AI-based prostate cancer detection pathway. 1 Alves et al []; . 2. Bosma et al []; . 3. Saha et al []; . 4. Hosseinzadeh et al []; . 5. Bleker et al []; . 6. Krüger-Stokke et al []; . 7. Roest et al []; .
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