Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Artificial intelligence for detection of prostate cancer in biopsies during active surveillance

Arvidsson, Ida LU orcid ; Svanemur, Edvard ; Marginean, Felicia LU orcid ; Simoulis, Athanasios LU orcid ; Overgaard, Niels Christian LU ; Åström, Kalle LU orcid ; Heyden, Anders LU orcid ; Krzyzanowska, Agnieszka LU and Bjartell, Anders LU (2024) In BJU International
Abstract

Objectives: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS). Patients and methods: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated. Results: The sensitivity... (More)

Objectives: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS). Patients and methods: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated. Results: The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment. Conclusion: The AI-based biopsy cancer detection algorithm could be used to reduce the pathologists’ workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI-based algorithm in digital pathology used to detect cancer in a cohort of patients on AS.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
active surveillance, artificial intelligence, deep learning, PRIAS, prostate cancer
in
BJU International
publisher
Wiley-Blackwell
external identifiers
  • scopus:85197400341
  • pmid:38961742
ISSN
1464-4096
DOI
10.1111/bju.16456
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 The Author(s). BJU International published by John Wiley & Sons Ltd on behalf of BJU International.
id
16628eff-c7e5-407e-b403-8e8bac2b293d
date added to LUP
2024-08-15 16:23:15
date last changed
2024-10-04 02:59:36
@article{16628eff-c7e5-407e-b403-8e8bac2b293d,
  abstract     = {{<p>Objectives: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS). Patients and methods: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated. Results: The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment. Conclusion: The AI-based biopsy cancer detection algorithm could be used to reduce the pathologists’ workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI-based algorithm in digital pathology used to detect cancer in a cohort of patients on AS.</p>}},
  author       = {{Arvidsson, Ida and Svanemur, Edvard and Marginean, Felicia and Simoulis, Athanasios and Overgaard, Niels Christian and Åström, Kalle and Heyden, Anders and Krzyzanowska, Agnieszka and Bjartell, Anders}},
  issn         = {{1464-4096}},
  keywords     = {{active surveillance; artificial intelligence; deep learning; PRIAS; prostate cancer}},
  language     = {{eng}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{BJU International}},
  title        = {{Artificial intelligence for detection of prostate cancer in biopsies during active surveillance}},
  url          = {{http://dx.doi.org/10.1111/bju.16456}},
  doi          = {{10.1111/bju.16456}},
  year         = {{2024}},
}