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

Thimansson, Erik ; Zackrisson, Sophia LU ; Jäderling, Fredrik ; Alterbeck, Max LU ; Jiborn, Thomas LU ; Bjartell, Anders LU and Wallström, Jonas (2024) In Acta Oncologica 63. p.816-821
Abstract

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.... (More)

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.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, Magnetic resonance imaging, overdiagnosis, prostatespecific antigen, prostatic neoplasms
in
Acta Oncologica
volume
63
pages
6 pages
publisher
Taylor & Francis
external identifiers
  • pmid:39473176
  • scopus:85208167411
ISSN
0284-186X
DOI
10.2340/1651-226X.2024.40475
language
English
LU publication?
yes
id
fb93a52b-01c5-4fc1-87db-e60907ab85a3
date added to LUP
2024-12-10 13:51:35
date last changed
2025-07-09 06:59:06
@article{fb93a52b-01c5-4fc1-87db-e60907ab85a3,
  abstract     = {{<p>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.</p>}},
  author       = {{Thimansson, Erik and Zackrisson, Sophia and Jäderling, Fredrik and Alterbeck, Max and Jiborn, Thomas and Bjartell, Anders and Wallström, Jonas}},
  issn         = {{0284-186X}},
  keywords     = {{artificial intelligence; Magnetic resonance imaging; overdiagnosis; prostatespecific antigen; prostatic neoplasms}},
  language     = {{eng}},
  pages        = {{816--821}},
  publisher    = {{Taylor & Francis}},
  series       = {{Acta Oncologica}},
  title        = {{A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing}},
  url          = {{http://dx.doi.org/10.2340/1651-226X.2024.40475}},
  doi          = {{10.2340/1651-226X.2024.40475}},
  volume       = {{63}},
  year         = {{2024}},
}