Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study
Artikel i vetenskaplig tidskrift, 2026
This study aimed to evaluate the clinical acceptability of artificial intelligence (AI)-based organ segmentations on pretreatment CT images of prostate cancer patients using manual organ delineations as a reference. Paired AI-based segmentations and manual delineations of the prostate, urinary bladder, and rectum were evaluated by three observers, according to a 4-grade Likert-scale, based on quality criteria, developed through a Delphi process. Visual grading characteristics (VGC) analysis was performed. When comparing the ratings of AI-based (n = 360) and manual delineations (n = 360), the area under the VGC-curve (AUCVGC) was 0.36 (95% CI 0.27–0.44), 0.35 (95% CI 0.28–0.41), and 0.3 (95% CI 0.22–0.40) for the prostate, urinary bladder, and rectum, respectively, indicating inferior ratings for the algorithm. Few AI segmentations (8%) were considered clinically unacceptable, while in 67% no or minor changes were needed. Despite superior ratings for manual delineations, most AI-segmentations needed no or minor changes, indicating clinical acceptability.
Författare
Eirini Polymeri
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Åse (Allansdotter) Johnsson
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Olof Enqvist
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Eigenvision AB
Johannes Ulén
Eigenvision AB
Jon Kindblom
Sahlgrenska universitetssjukhuset
Karin Braide
Göteborgs universitet
Hans Jurgen Wiltz
Region Kronoberg
Margareta Tanyasiová
Sahlgrenska universitetssjukhuset
E. Tragardh
Skånes universitetssjukhus (SUS)
L. Edenbrandt
Göteborgs universitet
Henrik Kjölhede
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Angelica Svalkvist
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Radiation Protection Dosimetry
0144-8420 (ISSN) 17423406 (eISSN)
Vol. 202 3-4 204-213Ämneskategorier (SSIF 2025)
Cancer och onkologi
Artificiell intelligens
DOI
10.1093/rpd/ncaf184