Emphysema subtyping on thoracic computed tomography scans using deep neural networks

• Published in: Scientific reports Vol. 13; no. 1; pp. 14147 - 11
• Main Authors: Xie, Weiyi, Jacobs, Colin, Charbonnier, Jean-Paul, Slebos, Dirk Jan, van Ginneken, Bram
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.08.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 631/114/2397; Algorithms; Chronic obstructive pulmonary disease; Computed tomography; Deep learning; Emphysema; Heterogeneity; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Thorax
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-40116-6

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.