TY - JOUR T1 - Probabilistic modeling of COPD imaging characteristics for disease severity prediction JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.congress-2022.2421 VL - 60 IS - suppl 66 SP - 2421 AU - S Dias Almeida AU - T Norajitra AU - C Lueth AU - T Wald AU - T Kopytova AU - M Nolden AU - P F. Jaeger AU - C Peter Heussel AU - J Biederer AU - H Kauczor AU - O Weinheimer AU - K Maier-Hein Y1 - 2022/09/04 UR - http://erj.ersjournals.com/content/60/suppl_66/2421.abstract N2 - Introduction: The heterogeneous imaging characteristics of COPD, with at least emphysema and airway types, hinder automated characterization and the development of disease prediction methods. Probabilistic models, such as Gaussian Mixture Models (GMM), successfully model disease heterogeneity, under the assumption that underlying data can be generated from a mixture of gaussians. This study aims to develop and evaluate a GMM approach to predict COPD severity, defined by the GOLD stage.Methods: Inspiratory computed tomography scans from 496 consecutive participants of the COSYCONET cohort study (65 ± 9 y.o.) were split into training (N=249), validation (N=123) and test set (N=124). Two state-of-the-art (SoA) methods and the GMM approach were compared to predict GOLD: a) percentage of low attenuation areas under -950 HU (LAA-950%); b) convolutional neural network (CNN) method1; c) GMM fitted on the mean lung density and LAA-950% of 3D lung patches (2.53 cm3) was first used to predict the presence of the disease, followed by patient-wise score aggregation for GOLD prediction. Area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) are reported on the test set as an evaluation metric.Results: View this table:Conclusion: The GMM probabilistic approach shows to be the best GOLD stage discriminator, as it seems to capture the population distribution better than SoA baselines. This supports the idea that out-of-distribution methods may be a promising next step for the identification and grading of heterogeneous diseases, such as COPD.1González, G. et al. Am J Respir Crit Care Med. 2018; 197(2):193-203FootnotesCite this article as Eur Respir J 2022; 60: Suppl. 66, 2421.This article was presented at the 2022 ERS International Congress, in session “-”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only). ER -