Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT.
Invest Radiol
; 59(3): 215-222, 2024 Mar 01.
Article
in En
| MEDLINE
| ID: mdl-37490031
OBJECTIVES: The aim of this study was to evaluate the accuracy of modern low-field magnetic resonance imaging (MRI) for lung nodule detection and to correlate nodule size measurement with computed tomography (CT) as reference. MATERIALS AND METHODS: Between November 2020 and July 2021, a prospective clinical trial using low-field MRI at 0.55 T was performed in patients with known pulmonary nodules from a single academic medical center. Every patient underwent MRI and CT imaging on the same day. The primary aim was to evaluate the detection accuracy of pulmonary nodules using MRI with transversal periodically rotated overlapping parallel lines with enhanced reconstruction in combination with coronal half-Fourier acquired single-shot turbo spin-echo MRI sequences. The secondary outcome was the correlation of the mean lung nodule diameter with CT as reference according to the Lung Imaging Reporting and Data System. Nonparametric Mann-Whitney U test, Spearman rank correlation coefficient, and Bland-Altman analysis were applied to analyze the results. RESULTS: A total of 46 participants (mean age ± SD, 66 ± 11 years; 26 women) were included. In a blinded analysis of 964 lung nodules, the detection accuracy was 100% for those ≥6 mm (126/126), 80% (159/200) for those ≥4-<6 mm, and 23% (147/638) for those <4 mm in MRI compared with reference CT. Spearman correlation coefficient of MRI and CT size measurement was r = 0.87 ( P < 0.001), and the mean difference was 0.16 ± 0.9 mm. CONCLUSIONS: Modern low-field MRI shows excellent accuracy in lesion detection for lung nodules ≥6 mm and a very strong correlation with CT imaging for size measurement, but could not compete with CT in the detection of small nodules.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Multiple Pulmonary Nodules
/
Lung Neoplasms
Type of study:
Diagnostic_studies
Limits:
Female
/
Humans
Language:
En
Journal:
Invest Radiol
Year:
2024
Document type:
Article
Country of publication: