Capturing COPD heterogeneity

• Verfasst von: Almeida, Silvia D. [VerfasserIn]
• Verfasst von: Norajitra, Tobias [VerfasserIn]
• Verfasst von: Lüth, Carsten T. [VerfasserIn]
• Verfasst von: Wald, Tassilo [VerfasserIn]
• Verfasst von: Weru, Vivienn [VerfasserIn]
• Verfasst von: Nolden, Marco [VerfasserIn]
• Verfasst von: Jäger, Paul F. [VerfasserIn]
• Verfasst von: Stackelberg, Oyunbileg von [VerfasserIn]
• Verfasst von: Heußel, Claus Peter [VerfasserIn]
• Verfasst von: Weinheimer, Oliver [VerfasserIn]
• Verfasst von: Biederer, Jürgen [VerfasserIn]
• Verfasst von: Kauczor, Hans-Ulrich [VerfasserIn]
• Verfasst von: Maier-Hein, Klaus H. [VerfasserIn]
• Titel: Capturing COPD heterogeneity
• Titelzusatz: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography
• Verf.angabe: Silvia D. Almeida, Tobias Norajitra, Carsten T. Lüth, Tassilo Wald, Vivienn Weru, Marco Nolden, Paul F. Jäger, Oyunbileg von Stackelberg, Claus Peter Heußel, Oliver Weinheimer, Jürgen Biederer, Hans-Ulrich Kauczor and Klaus Maier-Hein
• E-Jahr: 2024
• Jahr: 01 March 2024
• Umfang: 15 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 30.07.2024
• Titel Quelle: Enthalten in: Frontiers in medicine
• Ort Quelle: Lausanne : Frontiers Media, 2014
• Jahr Quelle: 2024
• Band/Heft Quelle: 11(2024), Artikel-ID 1360706, Seite 1-15
• ISSN Quelle: 2296-858X
• DOI: doi:10.3389/fmed.2024.1360706
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Airway disease
• Sach-SW: anomaly detection
• Sach-SW: Artificial intelligence (AI)
• Sach-SW: Chronic obstructive pulmonary disease (COPD)
• Sach-SW: Computed tomography (CT)
• Sach-SW: Emphysema
• Sach-SW: Gold
• K10plus-PPN: 189692526X

Abstract

Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRM Emph, functional small-airway disease: PRM fSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRM Emph, PRM fSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRM Emph, PRM fSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRM Emph (r = 0.66, p < 0.01) and PRM fSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.