Artificial intelligence-based emphysema quantification in routine chest computed tomography

• Verfasst von: Wiedbrauck, Damian [VerfasserIn]
• Verfasst von: Karczewski, Maciej [VerfasserIn]
• Verfasst von: Schönberg, Stefan [VerfasserIn]
• Verfasst von: Fink, Christian [VerfasserIn]
• Verfasst von: Kayed, Hany [VerfasserIn]
• Titel: Artificial intelligence-based emphysema quantification in routine chest computed tomography
• Titelzusatz: correlation with spirometry and visual emphysema grading
• Verf.angabe: Damian Wiedbrauck MD, Maciej Karczewski MSc, Stefan O. Schoenberg MD, Christian Fink MD, Hany Kayed MD
• E-Jahr: 2024
• Jahr: 5/6 2024
• Umfang: 6 S.
• Fussnoten: Gesehen am 26.11.2024
• Titel Quelle: Enthalten in: Journal of computer assisted tomography
• Ort Quelle: Philadelphia, Pa. : Lippincott Williams & Wilkins, 1977
• Jahr Quelle: 2024
• Band/Heft Quelle: 48(2024), 3, Seite 388-393
• ISSN Quelle: 1532-3145
• DOI: doi:10.1097/RCT.0000000000001572
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1909505285

Abstract

Objective - The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysema grades in routine chest computed tomography (CT). Furthermore, optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or moderate to more extensive visual emphysema grades were calculated. - Methods - In a retrospective study of 298 consecutive patients who underwent routine chest CT and spirometry examinations, LAV% was quantified using an AI-based software with a threshold < −950 HU. The FEV1/FVC was derived from spirometry, with FEV1/FVC < 70% indicating airway obstruction. The mean time interval of CT from spirometry was 3.87 ± 4.78 days. Severity of emphysema was visually graded by an experienced chest radiologist using an established 5-grade ordinal scale (Fleischner Society classification system). Spearman correlation coefficient between LAV% and FEV1/FVC was calculated. Receiver operating characteristic determined the optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or a visual emphysema grade of moderate or higher (Fleischner grade 3-5). - Results - Significant correlation between LAV% and FEV1/FVC was found (ϱ = −0.477, P < 0.001). Increasing LAV% corresponded to higher visual emphysema grades. For patients with absent visual emphysema, mean LAV% was 2.98 ± 3.30, for patients with trace emphysema 3.22 ± 2.75, for patients with mild emphysema 3.90 ± 3.33, for patients with moderate emphysema 6.41 ± 3.46, for patients with confluent emphysema 9.02 ± 5.45, and for patients with destructive emphysema 16.90 ± 8.19. Optimal LAV% cutoff value for predicting a FEV1/FVC < 70 was 6.1 (area under the curve = 0.764, sensitivity = 0.773, specificity = 0.665), while for predicting a visual emphysema grade of moderate or higher, it was 4.7 (area under the curve = 0.802, sensitivity = 0.766, specificity = 0.742). Furthermore, correlation between visual emphysema grading and FEV1/FVC was found. In patients with FEV1/FVC < 70% a high proportion of subjects had emphysema grade 3 (moderate) or higher, whereas in patients with FEV1/FVC ≥ 70%, a larger proportion had emphysema grade 3 (moderate) or lower. The sensitivity for visual emphysema grading predicting a FEV1/FVC < 70% was 56.3% with an optimal cutoff point at a visual grade of 4 (confluent), demonstrating a lower sensitivity compared with LAV% (77.3%). - Conclusions - A significant correlation between AI-based LAV% and FEV1/FVC as well as visual CT emphysema grades can be found in routine chest CT suggesting that AI-based LAV% measurement might be integrated as an add-on functional parameter in the evaluation of chest CT in the future.