Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.

Purpose: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease. Materials and Methods: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.3-28.6 weeks; 55 female and 48 male infants) with (n = 86) and without (n = 21) chronic lung disease (bronchopulmonary dysplasia [BPD]). Convolutional neural networks were developed for lung segmentation, and a three-dimensional reconstruction was used to calculate MRI features for lung volume, shape, pixel intensity, and surface. These features were explored as indicators of BPD and disease-associated lung structural remodeling through correlation with lung injury scores and multinomial models for BPD severity stratification. Results: The lung segmentation model reached a volumetric Dice coefficient of 0.908 in cross-validation and 0.880 on the independent test dataset, matching expert-level performance across disease grades. MRI lung features demonstrated significant correlations with lung injury scores and added structural information for the separation of neonates with BPD (BPD vs no BPD: average area under the receiver operating characteristic curve [AUC], 0.92 ± 0.02 [SD]; no or mild BPD vs moderate or severe BPD: average AUC, 0.84 ± 0.03). Conclusion: This study demonstrated high performance of DL models for MRI neonatal lung segmentation and showed the potential of automated MRI features for diagnostic assessment of neonatal lung disease while avoiding radiation exposure.Keywords: Bronchopulmonary Dysplasia, Chronic Lung Disease, Preterm Infant, Lung Segmentation, Lung MRI, BPD Severity Assessment, Deep Learning, Lung Imaging Biomarkers, Lung Topology Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Parraga and Sharma in this issue.

Early Changes and Indicators Characterizing Lung Aging in Neonatal Chronic Lung Disease.

Infants suffering from neonatal chronic lung disease, i.e., bronchopulmonary dysplasia, are facing long-term consequences determined by individual genetic background, presence of infections, and postnatal treatment strategies such as mechanical ventilation and oxygen toxicity. The adverse effects provoked by these measures include inflammatory processes, oxidative stress, altered growth factor signaling, and remodeling of the extracellular matrix. Both, acute and long-term consequences are determined by the capacity of the immature lung to respond to the challenges outlined above. The subsequent impairment of lung growth translates into an altered trajectory of lung function later in life. Here, knowledge about second and third hit events provoked through environmental insults are of specific importance when advocating lifestyle recommendations to this patient population. A profound exchange between the different health care professionals involved is urgently needed and needs to consider disease origin while future monitoring and treatment strategies are developed.