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ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.
Penzkofer, Tobias; Padhani, Anwar R; Turkbey, Baris; Haider, Masoom A; Huisman, Henkjan; Walz, Jochen; Salomon, Georg; Schoots, Ivo G; Richenberg, Jonathan; Villeirs, Geert; Panebianco, Valeria; Rouviere, Olivier; Logager, Vibeke Berg; Barentsz, Jelle.
Afiliação
  • Penzkofer T; Department of Radiology, Charité University Hospital, Augustenburger Platz 1, 13354, Berlin, Germany. tobias.penzkofer@charite.de.
  • Padhani AR; Berlin Institute of Health, Berlin, Germany. tobias.penzkofer@charite.de.
  • Turkbey B; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK.
  • Haider MA; Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
  • Huisman H; Joint Department of Medical Imaging, Sinai Health System, University Health Network, University of Toronto, Toronto, Canada.
  • Walz J; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Salomon G; Department of Urology, Institut Paoli-Calmettes Cancer Centre, Marseille, France.
  • Schoots IG; Martini-Klinik am UKE, University Hospital Hamburg, Hamburg, Germany.
  • Richenberg J; Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Villeirs G; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Panebianco V; Department of Imaging, BSUH NHS Trust, Brighton, UK.
  • Rouviere O; Department of Radiology and Nuclear Medicine, Ghent University Hospital, Ghent, Belgium.
  • Logager VB; Department of Radiological Sciences, Oncology and Pathology, Sapienza/Policlinico Umberto I, Rome, Italy.
  • Barentsz J; Department of Urinary and Vascular Imaging, Hospices Civils de Lyon, Lyon, France.
Eur Radiol ; 31(12): 9567-9578, 2021 Dec.
Article em En | MEDLINE | ID: mdl-33991226
Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Inteligência Artificial Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Inteligência Artificial Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article