Spatial Heterogeneity of Excess Lung Fluid in Cystic Fibrosis: Generalized, Localized Diffuse, and Localized Presentations
Source abstract
Abstract
Magnetic resonance (MR) imaging has demonstrated that CF subjects have a significantly higher lung density (e.g., fluid content) when compared with healthy control subjects, but, at present, there are no techniques to quantify the spatial presentation of these lung abnormalities. The excess fluid in MR lung images for CF subjects with mild (n = 4), moderate (n = 5), and severe (n = 4) disease and age- and sex-matched healthy controls (n = 13) in both the right and left lungs was identified and quantified using a thresholding-based image segmentation technique using healthy controls as a baseline. MR lung images were categorized into one of three spatial presentation groups based on their regional and global percent area of the lung covered by excess fluid (i.e., spatial distribution): (i) generalized for sparse, (ii) localized diffuse for a moderate focality, and (iii) localized for a strong focality. A total of 96% of the controls presented as generalized. CF subjects populated all three presentation groups and an individual’s right and left lungs did not always categorize identically. The developed metrics for categorization provide a quantification method to describe the spatial presentation of CF disease and suggests the heterogeneous nature of the disease.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cystic Fibrosis Research Advances · Pneumonia and Respiratory Infections · Neonatal Respiratory Health Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA
Department of Radiology, University of California, San Diego, CA 92093, USA
Funding
California State University Program for Education & Research in Biotechnology New Investigator · College of Sciences at San Diego State University · ACM SIGHPC Computational & Data Science Fellowship · NIH IMSD Program · NSF STEM Scholar Award · NIH
References (52)
Show source-provided references
1. Update in Cystic Fibrosis 201810.1164/rccm.201902-0310UP ↗
2. Role of epithelial HCO3− transport in mucin secretion: Lessons from cystic fibrosis10.1152/ajpcell.00362.2010 ↗
3. State of the art: Why do the lungs of patients with cystic fibrosis become infected and why can’t they clear the infection?10.1186/1465-9921-4-8 ↗
4. Putting lung function and physiology into perspective: Cystic fibrosis in adults10.1111/resp.12382 ↗
5. Reproducibility of a scoring system for computed tomography scanning in cystic fibrosis10.1097/01.rti.0000203937.82276.ce ↗
6. Cystic fibrosis genetics: From molecular understanding to clinical application10.1038/nrg3849 ↗
7. Quantification of the relative contribution of environmental and genetic factors to variation in cystic fibrosis lung function10.1016/j.jpeds.2010.05.018 ↗
8. Differences in CT density between dependent and nondependent portions of the lung: Influence of lung volume10.2214/ajr.161.4.8372744 ↗
9. Image analysis for cystic fibrosis: Computer-assisted airway wall and vessel measurements from low-dose, limited scan lung CT images10.1007/s10278-012-9476-4 ↗
10. Quantitative air-trapping analysis in children with mild cystic fibrosis lung disease10.1002/ppul.20091 ↗
11. The chest roentgenogram in cystic fibrosis: A new scoring system10.1542/peds.63.1.24 ↗
12. Estimation of cancer mortality associated with repetitive computed tomography scanning10.1164/rccm.200505-810OC ↗
13. Radiation doses and risks in chest computed tomography examinations10.1513/pats.200611-172HT ↗
14. Tracking Lung Clearance Index and chest CT in mild cystic fibrosis lung disease over a period of three years10.1016/j.rmed.2014.03.011 ↗
15. Cystic fibrosis: Are volumetric ultra-low-dose expiratory CT scans sufficient for monitoring related lung disease?10.1148/radiol.2532090306 ↗
16. Development of low-dose protocols for thin-section CT assessment of cystic fibrosis in pediatric patients10.1148/radiol.10100278 ↗
17. Magnetic resonance imaging of the lung in cystic fibrosis10.1513/pats.200611-181HT ↗
18. Computed Tomography and Magnetic Resonance Imaging in Cystic Fibrosis Lung Disease10.1002/jmri.22374 ↗
19. Morphologic and functional scoring of cystic fibrosis lung disease using MRI10.1016/j.ejrad.2011.02.045 ↗
20. Assessment of hemodynamic changes in the systemic and pulmonary arterial circulation in patients with cystic fibrosis using phase-contrast MRI10.1007/s00330-005-2721-1 ↗
21. Proton MRI appearance of cystic fibrosis: Comparison to CT10.1007/s00330-006-0373-4 ↗
22. Supine and prone differences in regional lung density and pleural pressure gradients in the human lung with constant shape10.1152/japplphysiol.00324.2009 ↗
23. Measuring Lung Water: Ex Vivo Validation of Multi-image Gradient Echo MRI10.1002/jmri.22600 ↗
24. Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends10.1148/rg.2015140232 ↗
25. Spirometric reference values from a sample of the general U.S. population10.1164/ajrccm.159.1.9712108 ↗
26. Quantitative MRI measurement of lung density must account for the change in T(2) (*) with lung inflation10.1002/jmri.21866 ↗
27. Inhaled nitric oxide alters the distribution of blood flow in the healthy human lung, suggesting active hypoxic pulmonary vasoconstriction in normoxia10.1152/japplphysiol.01354.2013 ↗
28. Glassner, A.S. (1990). Graphics Gems, Academic Press.10.1016/B978-0-08-050753-8.50133-9 ↗
29. CFTR modulator theratyping: Current status, gaps and future directions10.1016/j.jcf.2018.05.004 ↗
30. Ivacaftor: A novel gene-based therapeutic approach for cystic fibrosis
31. Progress in therapies for cystic fibrosis10.1016/S2213-2600(16)00023-0 ↗
32. Efficacy and safety of the elexacaftor plus tezacaftor plus ivacaftor combination regimen in people with cystic fibrosis homozygous for the F508del mutation: A double-blind, randomised, phase 3 trial10.1016/S0140-6736(19)32597-8 ↗
33. Elexacaftor–Tezacaftor–Ivacaftor for Cystic Firosis with a Single Phe508del Allele10.1056/NEJMoa1908639 ↗
34. (2020, May 11). Drug Approvals and Databases. Orkambi, Available online: https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases.
35. (2020, May 11). Drug Approvals and Databases. Symdeko 2018, Available online: https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases.
36. (2020, May 11). Drug Approvals and Databases. Trikafta, Available online: https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases.
37. Long-term computed tomographic changes in cystic fibrosis patients treated with ivacaftor10.1183/13993003.01918-2015 ↗
38. Ivacaftor Imaging Response in Cystic Fibrosis10.1164/rccm.201308-1433IM ↗
39. Improvement in bronchiectasis on CT imaging in a pediatric patient with cystic fibrosis on ivacaftor therapy10.1159/000365999 ↗
40. Computed tomography correlates with improvement with ivacaftor in cystic fibrosis patients with G551D mutation10.1016/j.jcf.2014.06.011 ↗
41. Fusion of 3D lung CT and serum biomarkers for diagnosis of multiple pathological types on pulmonary nodules10.1016/j.cmpb.2021.106381 ↗
42. Imaging research in fibrotic lung disease; applying deep learning to unsolved problems10.1016/S2213-2600(20)30003-5 ↗
43. Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores10.1148/ryct.2020200022 ↗
44. Radiomics and Computerized Analysis of CT Images: Looking Forward10.1148/ryct.2020200589 ↗
45. Kayal, S., Dubost, F., Tiddens, H.A.W.M., and de Bruijine, M. (2020, January 3–7). Spectral Data Augmentation Techniques to Quantify Lung Pathology from CT-Images. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.10.1109/ISBI45749.2020.9098581 ↗
46. Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis10.1183/09031936.00071812 ↗
47. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: A case-cohort study10.1016/S2213-2600(18)30286-8 ↗
48. Computer analysis of computed tomography scans of the lung: A survey10.1109/TMI.2005.862753 ↗
49. Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks10.1038/s41598-019-56989-5 ↗
50. Marques, F., Bubost, F., Kemmer-van de Corput, M., Tiddens, H.A.W., and de Bruijne, M. (2018). Quantification of lung abnormalities in cystic fibrosis using deep networks. arXiv.10.1117/12.2292188 ↗
51. Izonin, I., Tkachenko, R., Fedushko, S., Koziy, D., Zub, K., and Vovk, O. (2021). RBF-Based Input Doubling Method for Small Medical Data Processing, Springer. Lecture Notes on Data Engineering and Communications Technologies.10.1007/978-3-030-80475-6_3 ↗
52. A review of inflammatory mechanism in airway diseases10.1007/s00011-018-1191-2 ↗