Volume 26, Issue 9, September 2019, Pages 1191-1199
Maarten Lanclus, MSc,Johan Clukers, MD, Cedric Van Holsbeke, PhD, Wim Vos, PhD, Glenn Leemans, MSc, Birgit Holbrechts, MD, Katherine Barboza,PhD, Wilfried De Backer, PhD, Jan De Backer, PhD
Rationale and Objectives: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this dis-ease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression.
Materials and Methods: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data(unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms.
Results: A total of 11 baseline FRI parameters could significantly distinguish (p<0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support VectorMachines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline
Conclusion: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient-specific level in contrast to classical clinical parameters.
Key Words: Pulmonary disease, chronic obstructive; Disease progression; Support vector machine; Patient-specific modeling; Radio-graphic image interpretation, Computer-assisted.
© 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
Categorised in: Publication / September 1, 2019 11:14 am /Tags: Patient-specific modeling, Pulmonary disease