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ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging
- Source :
- EUROPEAN RADIOLOGY, European Radiology, 31, 9567-9578, European Radiology, European Radiology, 31(12), 9567-9578. Springer-Verlag, European Radiology, 31, 12, pp. 9567-9578, Penzkofer, T, Padhani, A R, Turkbey, B, Haider, M A, Huisman, H, Walz, J, Salomon, G, Schoots, I G, Richenberg, J, Villeirs, G, Panebianco, V, Rouviere, O, Logager, V B & Barentsz, J 2021, ' ESUR/ESUI position paper : developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging ', European Radiology, vol. 31, no. 12, pp. 9567-9578 . https://doi.org/10.1007/s00330-021-08021-6
- Publication Year :
- 2021
-
Abstract
- 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.
- Subjects :
- Image-Guided Biopsy
Male
Artificial intelligence
COMPUTER-AIDED DETECTION
PREDICTION
Disease
artificial intelligence
deep learning
image-guided biopsy
multiparametric magnetic resonance imaging
prostate cancer
Image-guided biopsy
030218 nuclear medicine & medical imaging
03 medical and health sciences
Prostate cancer
0302 clinical medicine
SDG 3 - Good Health and Well-being
Multiparametric magnetic resonance imaging
Medicine and Health Sciences
Medicine
Humans
Radiology, Nuclear Medicine and imaging
Neuroradiology
PI-RADS
medicine.diagnostic_test
business.industry
Prostatic Neoplasms
Interventional radiology
Urogenital
Deep learning
General Medicine
PERFORMANCE
medicine.disease
Magnetic Resonance Imaging
Workflow
Software deployment
030220 oncology & carcinogenesis
Urological cancers Radboud Institute for Health Sciences [Radboudumc 15]
BIOPSY
Position paper
business
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
MRI
Subjects
Details
- ISSN :
- 09387994 and 14321084
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- European Radiology
- Accession number :
- edsair.doi.dedup.....83258d73eb06d280b9d4a3701c9850c1