| Online-Ressource |
Verfasst von: | Wiedbrauck, Damian [VerfasserIn]  |
| Karczewski, Maciej [VerfasserIn]  |
| Schönberg, Stefan [VerfasserIn]  |
| Fink, Christian [VerfasserIn]  |
| 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 |
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. |
DOI: | doi:10.1097/RCT.0000000000001572 |
URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1097/RCT.0000000000001572 |
| Volltext: https://journals.lww.com/jcat/abstract/2024/05000/artificial_intelligence_based_emphysema.7.aspx |
| DOI: https://doi.org/10.1097/RCT.0000000000001572 |
Datenträger: | Online-Ressource |
Sprache: | eng |
K10plus-PPN: | 1909505285 |
Verknüpfungen: | → Zeitschrift |
Artificial intelligence-based emphysema quantification in routine chest computed tomography / Wiedbrauck, Damian [VerfasserIn]; 5/6 2024 (Online-Ressource)