Abstract

To compare postnatal magnetic resonance imaging (MRI) with the reference standard computed tomography (CT) in the identification of the key features for diagnosing different types of congenital lung malformation (CLM). Respiratory-triggered T2-weighted single-shot turbo spin echo (ss-TSE), respiratory-triggered T1-weighted turbo field echo (TFE), balanced fast field echo (BFFE), and T2-weighted MultiVane sequences were performed at 1.5T on 20 patients prospectively enrolled. Two independent radiologists examined the postnatal CT and MRI evaluating the presence of cysts, hyperinflation, solid component, abnormal arteries and/or venous drainage, and bronchocele. Diagnostic performance of MRI was calculated and the agreement between the findings was assessed using the McNemar-Bowker test. Interobserver agreement was measured with the kappa coefficient. CT reported five congenital pulmonary airway malformations (CPAMs), eight segmental bronchial atresias, five bronchopulmonary sequestrations (BPS), one congenital lobar overinflation, one bronchogenic cyst, and three hybrid lesions. MRI reported the correct diagnosis in 19/20 (95%) patients and the malformation was correctly classified in 22/23 cases (96%). MRI correctly identified all the key findings described on the CT except for the abnormal vascularization (85.7% sensitivity, 100% specificity, 100% PPV, 94.1% NPV, 95% accuracy for arterial vessels; 57.1% sensitivity, 100% specificity, 100% PPV, 84.2% NPV, 87% accuracy for venous drainage). MRI can represent an effective alternative to CT in the postnatal assessment of CLM. In order to further narrow the gap with CT, the use of contrast material and improvements in sequence design are needed to obtain detailed information on vascularization, which is essential for surgical planning. • Congenital lung malformations (CLMs) can be effectively studied by MRI avoiding radiation exposure. • Crucial features of CLM have similar appearance when comparing CT with MRI. • MRI performs very well in CLM except for aberrant vessel detection and characterization.

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