Longitudinal Assessment of Patients With Cystic Fibrosis Lung Disease With Multivolume Noncontrast MRI and Spirometry

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study

BACKGROUND

Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.

PURPOSE

Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center.

STUDY TYPE

Retrospective.

POPULATION

A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.

FIELD STRENGTH/SEQUENCE

1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI.

ASSESSMENT

2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.

STATISTICAL TESTS

Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant.

RESULTS

The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data.

DATA CONCLUSION

The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

A successful treatment of a lobar atelectasis in a patient with cystic fibrosis

Lobar atelectasis may be a complication of pulmonary exacerbations in cystic fibrosis (CF). There are no established guidelines on the management of this condition in patients with CF. Therapeutic bronchoscopy with recombinant human deoxyribonuclease (rhDNase) instillation has been described to be successful in patients not responding to conservative measures. We describe a case of a young man with CF, with previously mild impaired lung function, presenting with cough, desaturation, and worsening dyspnea, persisting for over 6 weeks, despite conservative therapy. Thoracic imaging showed right lower lobe atelectasis, which was successfully treated with bronchoscopy and instillation of rhDNase. Long-term resolution of the atelectasis was confirmed with chest magnetic resonance imaging follow-up.

Cystic fibrosis year in review 2020: Section 2 pulmonary disease, infections, and inflammation

The outlook for those with cystic fibrosis (CF) has never been brighter with ever increasing life expectancy and the approval of the highly effective CFTR modulators, such as elexacaftor/tezacaftor/ivacaftor. With that being said, the progressive pulmonary decline and importance of lung health, infection, and inflammation in CF remains. This review is the second part in a three-part CF Year in Review 2020. Part one focused on the literature related to CFTR modulators while part three will feature the multisystem effects related to CF. This review focuses on articles from Pediatric Pulmonology, including articles from other journals that are of particular interest to clinicians. Herein, we highlight studies published during 2020 related to CF pulmonary disease, infection, treatment, and diagnostics.