Automated quantification of airway wall thickness on chest CT using retina U-Nets – Performance evaluation and application to a large cohort of chest CTs of COPD patients. Issue 155 (October 2022)

• Record Type: Journal Article
• Authors: Weikert, Thomas; Friebe, Liene; Wilder-Smith, Adrian; Yang, Shan; Sperl, Jonathan I.; Neumann, Dominik; Balachandran, Abishek; Bremerich, Jens; Sauter, Alexander W.
• Is Part Of: European journal of radiology. Issue 155(2022)
• Publication Date: 2022-10
• Subjects: COPD -- Deep learning -- U-Net -- Computed tomography -- Airway wall thickness -- Imaging biomarker; AUC Area under the curve -- AWT Airway Wall Thickness -- CNN Convolutional Neural Network -- COPD Chronic Obstructive Pulmonary Disease -- %LAV-950 Low Attenuation Volume (threshold −950 HU) -- NECT Non-contrast enhanced CT -- POI Point-of-interest -- ROC curve Receiver operating characteristic curve; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110460
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: An AI algorithm pipeline allows for automated measurement of airway wall thickness on CT. Walls of airway generations 3–8 were significantly thicker in COPD patients compared to controls. A classifier combining average airway wall thickness with an emphysema score was successfully differentiated CTs of COPD patients from CTs of controls. Airway wall thickness could complement the CT emphysema scoring (%LAV-950) in lung disease in the future. Abstract: Purpose: Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). Materials and methods: This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019–12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D- U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3–8 (AWT3-8 ) across the lungs. Mean AWT3- 8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. Results: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). Conclusion: Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. Clinical relevance statement: Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.