guest ::
| Home > Publications Database > Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRI. |
| Home > Publications Database > Metastasis Detection Using True and Artificial T1-Weighted Postcontrast Images in Brain MRI. |
| Journal Article | DZNE-2025-00497 |
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
2025
Lippincott Williams & Wilkins
Philadelphia, Pa.
This record in other databases: 
Please use a persistent id in citations: doi:10.1097/RLI.0000000000001137
Abstract: Small lesions are the limiting factor for reducing gadolinium-based contrast agents in brain magnetic resonance imaging (MRI). The purpose of this study was to compare the sensitivity and precision in metastasis detection on true contrast-enhanced T1-weighted (T1w) images and artificial images synthesized by a deep learning method using low-dose images.In this prospective, multicenter study (5 centers, 12 scanners), 917 participants underwent brain MRI between October 2021 and March 2023 including T1w low-dose (0.033 mmol/kg) and full-dose (0.1 mmol/kg) images. Forty participants with metastases or unremarkable brain findings were evaluated in a reading (mean age ± SD, 54.3 ± 15.1 years; 24 men). True and artificial T1w images were assessed for metastases in random order with 4 weeks between readings by 2 neuroradiologists. A reference reader reviewed all data to confirm metastases. Performances were compared using mid- P McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.The reference reader identified 97 metastases. The sensitivity of reader 1 did not differ significantly between sequences (sensitivity [precision]: true, 66.0% [98.5%]; artificial, 61.9% [98.4%]; P = 0.38). With a lower precision than reader 1, reader 2 found significantly more metastases using true images (sensitivity [precision]: true, 78.4% [87.4%]; artificial, 60.8% [80.8%]; P < 0.001). There was no significant difference in sensitivity for metastases ≥5 mm. The number of false-positive findings did not differ significantly between sequences.One reader showed a significantly higher overall sensitivity using true images. The similar detection performance for metastases ≥5 mm is promising for applying low-dose imaging in less challenging diagnostic tasks than metastasis detection.
Keyword(s): Humans (MeSH) ; Brain Neoplasms: diagnostic imaging (MeSH) ; Brain Neoplasms: secondary (MeSH) ; Male (MeSH) ; Middle Aged (MeSH) ; Female (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Prospective Studies (MeSH) ; Contrast Media: administration & dosage (MeSH) ; Sensitivity and Specificity (MeSH) ; Aged (MeSH) ; Adult (MeSH) ; Deep Learning (MeSH) ; convolutional neural network ; deep learning ; dose reduction ; gadolinium-based contrast agent ; low-dose ; magnetic resonance imaging ; metastasis detection ; virtual contrast ; Contrast Media
; Allianz-Lizenz ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |