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Portrait of Sophia Zackrisson. Photo

Sophia Zackrisson

Research group manager, Principal investigator, Professor, MD

Portrait of Sophia Zackrisson. Photo

A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing

Author

  • Erik Thimansson
  • Sophia Zackrisson
  • Fredrik Jäderling
  • Max Alterbeck
  • Thomas Jiborn
  • Anders Bjartell
  • Jonas Wallström

Summary, in English

Objectives: To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT). Methods: Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection were compared with assessments performed by on-site radiologists and reference radiologists. Per patient PI-RADS (Prostate Imaging-Reporting and Data System)/Likert scores were cross-tabulated and compared with biopsy outcomes, if performed. Positive MRI was defined as PI-RADS/Likert ≥4. Reader variability was assessed with weighted kappa scores. Results: The number of positive MRIs was 13 (23%), 8 (14%), and 29 (51%) for the local radiologists, expert consensus, and DL, respectively. Kappa scores were moderate for local radiologists versus expert consensus 0.55 (95% confidence interval [CI]: 0.37–0.74), slight for local radiologists versus DL 0.12 (95% CI: −0.07 to 0.32), and slight for expert consensus versus DL 0.17 (95% CI: −0.01 to 0.35). Out of 10 cases with biopsy proven prostate cancer with Gleason ≥3+4 the DL scored 7 as Likert ≥4. Interpretation: The Dl-algorithm showed low agreement with both local and expert radiologists. Training and validation of DL-algorithms in specific screening cohorts is essential before introduction in organized testing.

Department/s

  • Radiology Diagnostics, Malmö
  • LUCC: Lund University Cancer Centre
  • EpiHealth: Epidemiology for Health
  • LTH Profile Area: Photon Science and Technology
  • LU Profile Area: Light and Materials
  • Urological cancer, Malmö
  • eSSENCE: The e-Science Collaboration
  • Division of Translational Cancer Research

Publishing year

2024

Language

English

Pages

816-821

Publication/Series

Acta Oncologica

Volume

63

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Radiology and Medical Imaging

Keywords

  • artificial intelligence
  • Magnetic resonance imaging
  • overdiagnosis
  • prostatespecific antigen
  • prostatic neoplasms

Status

Published

Research group

  • Radiology Diagnostics, Malmö
  • Urological cancer, Malmö

ISBN/ISSN/Other

  • ISSN: 0284-186X