Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients with Low-grade Cancer.

Abnormal interpretation rate Cancer detection rate PI-RADS Performance metric Prostate cancer

Journal

Journal of the American College of Radiology : JACR
ISSN: 1558-349X
Titre abrégé: J Am Coll Radiol
Pays: United States
ID NLM: 101190326

Informations de publication

Date de publication:
12 Oct 2023
Historique:
received: 18 05 2023
revised: 21 07 2023
accepted: 27 07 2023
medline: 15 10 2023
pubmed: 15 10 2023
entrez: 14 10 2023
Statut: aheadofprint

Résumé

To evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa). This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest PI-RADS score and pathological diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa: Gleason score ≥7). The metrics AIR, CDR, and CDR adjusted for pathological confirmation rate (aCDR) were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa. A total of 3207 exams were interpreted by 33 radiologists. Overall AIR, CDR, and aCDR at PI-RADS 3-5 (4-5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3-5 (4-5) were in the 36.8-75.6% (21.9-57.5%) range and the 16.3-28.7% (10.9-26.5%) range. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line. We proposed CDR and AIR as performance metrics in prostate MRI and reported reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

Identifiants

pubmed: 37838189
pii: S1546-1440(23)00763-9
doi: 10.1016/j.jacr.2023.07.030
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Hirotsugu Nakai (H)

Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Hiroki Nagayama (H)

Department of Radiology, Mayo Clinic, Rochester, MN, United States; Department of Radiology, Nagasaki University School of Medicine, Nagasaki, Japan.

Hiroaki Takahashi (H)

Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Adam T Froemming (AT)

Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Akira Kawashima (A)

Department of Radiology, Mayo Clinic, Scottsdale, AZ, United States.

Candice W Bolan (CW)

Department of Radiology, Mayo Clinic, Jacksonville, FL, United States.

Daniel A Adamo (DA)

Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Rickey E Carter (RE)

Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States.

Robert T Fazzio (RT)

Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Shintaro Tsuji (S)

Mayo Clinic College of Medicine, Rochester, MN, United States.

Derek J Lomas (DJ)

Department of Urology, Mayo Clinic, Rochester, MN, United States.

Lance A Mynderse (LA)

Department of Urology, Mayo Clinic, Rochester, MN, United States.

Mitchell R Humphreys (MR)

Department of Urology, Mayo Clinic, Scottsdale, AZ, United States.

Chandler Dora (C)

Department of Urology, Mayo Clinic, Jacksonville, FL, United States.

Naoki Takahashi (N)

Department of Radiology, Mayo Clinic, Rochester, MN, United States. Electronic address: Takahashi.Naoki@mayo.edu.

Classifications MeSH