Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.


Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
07 2021
Historique:
received: 05 04 2021
revised: 29 06 2021
accepted: 15 07 2021
pubmed: 17 8 2021
medline: 14 4 2022
entrez: 16 8 2021
Statut: ppublish

Résumé

Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under- and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. PATIENT SUMMARY: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.

Identifiants

pubmed: 34393083
pii: S2405-4569(21)00181-4
doi: 10.1016/j.euf.2021.07.002
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

687-691

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Kimmo Kartasalo (K)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Wouter Bulten (W)

Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Brett Delahunt (B)

Department of Pathology and Molecular Medicine, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand.

Po-Hsuan Cameron Chen (PC)

Google Health, Palo Alto, CA, USA.

Hans Pinckaers (H)

Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Henrik Olsson (H)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Xiaoyi Ji (X)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Nita Mulliqi (N)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Hemamali Samaratunga (H)

Aquesta Uropathology and University of Queensland, Brisbane, QLD, Australia.

Toyonori Tsuzuki (T)

Department of Surgical Pathology, School of Medicine, Aichi Medical University, Nagakute, Japan.

Johan Lindberg (J)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Mattias Rantalainen (M)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Carolina Wählby (C)

Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden; BioImage Informatics Facility of SciLifeLab, Uppsala, Sweden.

Geert Litjens (G)

Department of Pathology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Pekka Ruusuvuori (P)

Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Institute of Biomedicine, Cancer Research Unit and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland.

Lars Egevad (L)

Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.

Martin Eklund (M)

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: martin.eklund@ki.se.

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Classifications MeSH