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Risk Stratification and Artificial Intelligence in Early Magnetic Resonance Imaging–based Detection of Prostate Cancer
- Source :
- European Urology Focus, 8, 1187-1191, European Urology Focus, 8, 5, pp. 1187-1191
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Magnetic resonance imaging (MRI) has transformed the diagnostic pathway for prostate cancer and now plays an upfront role before prostate biopsies. If a suspicious lesion is found on MRI, the subsequent biopsy can be targeted. A sharp increase is expected in the number of men who will undergo prostate MRI. The challenge is to provide good image quality and diagnostic accuracy while meeting the demands of the expected higher workload. A possible solution to this challenge is to include a suitable risk stratification tool before imaging. Other solutions, such as smarter and shorter MRI protocols, need to be explored. For most of these solutions, artificial intelligence (AI) can play an important role. AI applications have the potential to improve the diagnostic quality of the prostate MRI pathway and speed up the work. PATIENT SUMMARY: The use of prostate magnetic resonance imaging (MRI) for diagnosis of prostate cancer is increasing. Risk stratification of patients before imaging and the use of shorter scan protocols can help in managing MRI resources. Artificial intelligence can also play a role in automating some tasks. ispartof: EUROPEAN UROLOGY FOCUS vol:8 issue:5 pages:1187-1191 ispartof: location:Netherlands status: published
- Subjects :
- Male
Image-Guided Biopsy
Artificial intelligence
Prostate cancer
Urology
Prostatic Neoplasms
Magnetic Resonance Imaging
Risk Assessment
Magnetic resonance imaging
Artificial Intelligence
Urological cancers Radboud Institute for Health Sciences [Radboudumc 15]
Diagnosis
Humans
Risk stratification
Early Detection of Cancer
Subjects
Details
- ISSN :
- 24054569
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- European Urology Focus
- Accession number :
- edsair.doi.dedup.....9e402a47aeec0f01743bc5f1d964b748
- Full Text :
- https://doi.org/10.1016/j.euf.2021.11.005