External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.
MR Imaging
Prostate
Urinary
Journal
Radiology. Imaging cancer
ISSN: 2638-616X
Titre abrégé: Radiol Imaging Cancer
Pays: United States
ID NLM: 101765309
Informations de publication
Date de publication:
Nov 2024
Nov 2024
Historique:
medline:
14
10
2024
pubmed:
14
10
2024
entrez:
14
10
2024
Statut:
ppublish
Résumé
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL
Identifiants
pubmed: 39400232
doi: 10.1148/rycan.240050
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM