Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer.

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Title: Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer.
Authors: Delberghe F; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands. f.t.delberghe@tue.nl., Li X; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., van den Kroonenberg DL; Amsterdam UMC, Department of Urology, Boelelaan, 1117, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Boelelaan, 1117, Amsterdam, The Netherlands., Turco S; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., Zwart W; Angiogenesis Analytics, Den Bosch, The Netherlands., Valvano G; Angiogenesis Analytics, Den Bosch, The Netherlands., Jager A; Amsterdam UMC, Department of Urology, Boelelaan, 1117, Amsterdam, The Netherlands., Postema AW; Leiden University Medical Center, Department of Urology, Leiden, The Netherlands., Wijkstra H; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., Oddens JR; Amsterdam UMC, Department of Urology, Boelelaan, 1117, Amsterdam, The Netherlands.; Cancer Center Amsterdam, Boelelaan, 1117, Amsterdam, The Netherlands., Mischi M; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands.
Source: European radiology [Eur Radiol] 2026 Jun; Vol. 36 (6), pp. 4539-4550. Date of Electronic Publication: 2026 Jan 30.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-1084
DOI:10.1007/s00330-026-12323-y