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Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI-algorithm for lesion detection and classification.

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第一作者： Benedict,Oerther

第一单位： Department of Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

作者： Benedict,Oerther ^[1] ; Hannes,Engel ^[1] ; Andrea,Nedelcu ^[1] ; Christopher L,Schlett ^[1] ; Robert,Grimm ^[2] ; Heinrich,von Busch ^[3] ; August,Sigle ^[4] ; Christian,Gratzke ^[4] ; Fabian,Bamberg ^[1] ; Matthias,Benndorf ^[1]

作者单位： Department of Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. ^[1] MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany. ^[2] Diagnostic Imaging Digital & Automation, Siemens Healthcare GmbH, Erlangen, Germany. ^[3] Department of Urology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. ^[4]

关键词 PI-RADSv2.1 active surveillance artificial intelligence clinically significant prostate cancer lesion detection and classification prostate cancer

医学主题词男(雄)性(Male);人类(Humans);前列腺肿瘤(Prostatic Neoplasms);磁共振成像(Magnetic Resonance Imaging);回顾性研究(Retrospective Studies);观察性等待(Watchful Waiting);人工智能(Artificial Intelligence)

DOI 10.1002/pros.24528

PMID 36959777

发布时间 2023-05-22

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