Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Fischer, Andreas M.; Varga-Szemes, Akos; Martin, Simon S.; Sperl, Jonathan I.; Sahbaee, Pooyan; Neumann, Dominik; Gawlitza, Joshua; Henzler, Thomas; Johnson, Colin M.; Nance, John W.; Schoenberg, Stefan O.; Schoepf, U. Joseph
• Is Part Of: Journal of thoracic imaging. Volume 35:Issue 3(2020)Supplement 1
• Publication Date: 2020-05
• Subjects: artificial intelligence -- emphysema -- chronic obstructive pulmonary disease -- quantified computed tomography; Chest -- Radiography -- Periodicals; Chest -- Diseases -- Diagnosis -- Periodicals; 617.540757
• DOI: 10.1097/RTI.0000000000000500
• Languages: English
• ISSNs: 0883-5993

Abstract

Abstract : Objectives: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. Methods: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. Results: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P <0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P <0.0001), and the weakest in the left lower lobe (ρ=0.72, P <0.0001) and right middle lobe (ρ=0.72, P <0.0001). Conclusions: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.