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

Journal: European urology focus
Published Date:

Abstract

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.

Authors

  • Kimmo Kartasalo
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Wouter Bulten
    Diagnostic Image Analysis Group and the Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Brett Delahunt
    Department of Pathology and Molecular Medicine, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand.
  • Po-Hsuan Cameron Chen
    Google Health, Palo Alto, CA USA.
  • Hans Pinckaers
    Artera, Inc., Los Altos, CA.
  • Henrik Olsson
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Xiaoyi Ji
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Nita Mulliqi
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Hemamali Samaratunga
    Aquesta Uropathology and University of Queensland, Brisbane, QLD, Australia.
  • Toyonori Tsuzuki
    Department of Surgical Pathology, Aichi Medical University Hospital, Aichi 480-1195, Japan. tsuzuki@aichi-med-u.ac.jp.
  • Johan Lindberg
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Mattias Rantalainen
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Carolina Wählby
    1 Centre for Image Analysis/SciLifeLab, Uppsala University, Uppsala, Sweden.
  • Geert Litjens
    Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Pekka Ruusuvuori
    BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
  • Lars Egevad
    Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
  • Martin Eklund
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: martin.eklund@ki.se.