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2 April 2024 Detection of local emphysema progression using conditional CNN
Ariel H. Curiale, Raúl San José Estépar
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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262Z (2024) https://doi.org/10.1117/12.3006943
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
Emphysema progression in COPD is highly variable, and there is a lack of prognostic markers that describe the evolution of the disease. We hypothesize that local CT features can provide suitable information for describing the evolution of emphysema. In this work, we propose a deep learning approach to identify “deep local phenotypes” by learning the evolution of lung density and emphysema progression at a timed event using longitudinal autoencoders. Results show that there are local traits, or deep local phenotypes, that can predict the 5-year evolution of the local density patterns. Local features learned from the latent space representation of the emphysema progression seem to provide prognostic markers of progression due to the good performance in identifying regions (32 by 32 pixels) with and without emphysema progression. The model’s performance on our test dataset (n=364 millon of regions from 295 subjects) was AUC5%=0.87, PPV5%=0.57, Sens.5%= 0.55, ACC5%=0.84, Balanced ACC5% = 0.73, F15%=0.56 and K5%=0.46. Prognostication tools to detect local patterns of emphysema progression could be essential to manage COPD patients better, as there are no therapeutic options to reverse (or slow down) emphysema damage. Our approach could also improve the selection of patients in much-needed emphysema treatment trials.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ariel H. Curiale and Raúl San José Estépar "Detection of local emphysema progression using conditional CNN", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262Z (2 April 2024); https://doi.org/10.1117/12.3006943
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KEYWORDS
Emphysema
Lung
Chronic obstructive pulmonary disease
Computed tomography
Deep learning
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