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

Autor:	Thimansson E; Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden; Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden. per_erik.thimansson@med.lu.se., Zackrisson S; Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden; Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden., Jäderling F; Department of Radiology, Capio S:t Görans Hospital, Stockholm, Sweden; Institution of Molecular Medicine and Surgery (MMK), Karolinska Institutet, Stockholm, Sweden., Alterbeck M; Department of Translational Medicine, Urological Cancers, Lund University, Malmö, Sweden; Department of Urology, Skåne University Hospital, Malmö, Sweden., Jiborn T; Department of Urology, Helsingborg Hospital, Helsingborg, Sweden., Bjartell A; Department of Translational Medicine, Urological Cancers, Lund University, Malmö, Sweden; Department of Urology, Skåne University Hospital, Malmö, Sweden., Wallström J; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden; Sahlgrenska University Hospital, Gothenburg, Sweden.
Jazyk:	angličtina
Zdroj:	Acta oncologica (Stockholm, Sweden) [Acta Oncol] 2024 Oct 29; Vol. 63, pp. 816-821. Date of Electronic Publication: 2024 Oct 29.
DOI:	10.2340/1651-226X.2024.40475
Abstrakt:	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.
Databáze:	MEDLINE
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