External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.

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Bibliographic Details
Title: External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.
Authors: Johnson PM; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA. patricia.johnson3@nyulangone.org., Tong A; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA., Ginocchio L; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA., Del Hoyo JL; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA., Smereka P; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA., Harmon SA; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Turkbey B; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA., Chandarana H; Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Source: European radiology [Eur Radiol] 2026 Feb; Vol. 36 (2), pp. 1084-1092. Date of Electronic Publication: 2025 Aug 03.
Publication Type: Journal Article
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/s00330-025-11865-x