Journal article
Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow
Radiology. Cardiothoracic imaging, Vol.4(6), e210311
12/01/2022
DOI: 10.1148/ryct.210311
PMCID: PMC9806731
PMID: 36601453
Abstract
Purpose: To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods: In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard-and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014–2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018–2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2003 Dutch–Belgian Randomized Lung Cancer Screening trial (n = 55) acquired by using different CT scanners. Performance measures for different methods were computed and compared by using the Wilcoxon signed rank test. Results: At low-dose CT, 56 294 of 62 480 (90.1%) airways of the reference total airway count (TAC) and 32 109 of 37 864 (84.8%) airways of the peripheral TAC (TACp ), detected at standard-dose CT, were detected. Significant losses (P < .001) of 14 526 of 76 453 (19.0%) airways and 884 of 6908 (12.8%) airways in the TAC and 12 256 of 43 462 (28.2%) airways and 699 of 3882 (18.0%) airways in the TACp were observed, respectively, for the multiprotocol and multiscanner data without retraining. When using the automated low-dose CT method, TAC values of 347, 342, 323, and 266 and TACp values of 205, 202, 289, and 141 were observed for those who have never smoked and participants at Global Initiative for Chronic Obstructive Lung Disease stages 0, 1, and 2, respec-tively, which were superior to the respective values previously reported for matching groups when using a semiautomated method at standard-dose CT. Conclusion: CT. A low-cost, automated CT-based airway detection method was suitable for investigation of airway phenotypes at low-dose Clinical trial registration no. NCT00608764.
Details
- Title: Subtitle
- Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow
- Creators
- Syed Ahmed Nadeem - Roy J. and Lucille A. Carver College of MedicineAlejandro P. Comellas - Roy J. and Lucille A. Carver College of MedicineEric A. Hoffman - Roy J. and Lucille A. Carver College of MedicinePunam K. Saha - Roy J. and Lucille A. Carver College of Medicine
- Resource Type
- Journal article
- Publication Details
- Radiology. Cardiothoracic imaging, Vol.4(6), e210311
- DOI
- 10.1148/ryct.210311
- PMID
- 36601453
- PMCID
- PMC9806731
- ISSN
- 2638-6135
- eISSN
- 2638-6135
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health, award: R01 HL142042, 5U01 HL089897, R01 HL112986
- Language
- English
- Date published
- 12/01/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Internal Medicine
- Record Identifier
- 9984353644202771
Metrics
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