Abstract #1884

Hyperpolarized Ventilation MRI and Ensemble Machine Learning Predict Airflow Limitation Worsening in Ex-smokers

Cathy Ong-Ly^1,2, Andrew Westcott^1,2, Inderdeep Dhaliwal³, Aaron Fenster^1,2, Miranda Kirby⁴, and Grace Parraga^1,2,3

¹Robarts Research Institute, London, ON, Canada, ²Medical Biophysics, Western University, London, ON, Canada, ³Division of Respirology, Department of Medicine, Western University, London, ON, Canada, ⁴Physics, Ryerson University, Toronto, ON, Canada

Hyperpolarized noble-gas pulmonary imaging provides a way to measure ventilation and perfusion in patients. The potential of highly sensitive MRI biomarkers of lung function has not yet been exploited using machine-learning. Ensemble machine-learning merges diverse classifiers to improve classification accuracy and reduce the potential for misclassification. Our aim was to evaluate the performance of ensemble machine-learning algorithms and hyperpolarized gas MRI features for predicting worsening airflow measured using spirometry. This proof-of-concept study revealed that MRI ventilation combined with ensemble machine-learning predicted small changes in airflow limitation (∆FEV₁%_pred=5%) over relatively short time period (2.5 yr) in ex-smokers with and without COPD.

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