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ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging
ABSTRACT: 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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589789/ https://www.ncbi.nlm.nih.gov/pubmed/33991226 http://dx.doi.org/10.1007/s00330-021-08021-6 |
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author | 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 |
author_facet | 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 |
author_sort | Penzkofer, Tobias |
collection | PubMed |
description | ABSTRACT: 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. |
format | Online Article Text |
id | pubmed-8589789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85897892021-11-15 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 Eur Radiol Urogenital ABSTRACT: 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. Springer Berlin Heidelberg 2021-05-15 2021 /pmc/articles/PMC8589789/ /pubmed/33991226 http://dx.doi.org/10.1007/s00330-021-08021-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Urogenital 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 ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title | ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title_full | ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title_fullStr | ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title_full_unstemmed | ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title_short | ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
title_sort | esur/esui position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging |
topic | Urogenital |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589789/ https://www.ncbi.nlm.nih.gov/pubmed/33991226 http://dx.doi.org/10.1007/s00330-021-08021-6 |
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