Objective: Our objective was to train machine-learning algorithms on hyperpolarized 3He magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with chronic obstructive pulmonary disease (COPD). We hypothesized that hyperpolarized gas MRI ventilation, machine-learning and multivariate modelling could be combined to explain clinically relevant changes in forced expiratory volume in 1 sec (FEV1) over a relatively short, three year time period. Methods: Hyperpolarized 3He MRI was acquired using a coronal Cartesian FGRE sequence with a partial echo and segmented using a k-means cluster algorithm. A maximum entropy mask was used to generate a region of interest for texture feature extraction using a custom-built algorithm and PyRadiomics platform. Forward logistic-regression and principal-component-analysis were used for feature selection. Ensemble-based and single machine-learning classifiers were utilized; accuracies were evaluated using a confusion-matrix and area under the curve (AUC) of a sensitivityspecificity plot. Results: We evaluated 42 COPD patients with three year follow-up data, 27 of whom (9 Females/18 Males, 66±7 years) reported negligible changes in FEV1 and 15 participants (5 Females/10 Males, 71±8 years) reported worsening FEV1 greater than -5%pred, 30±8 months later. We generated a predictive model to explain FEV1 decline using bagged-trees trained on four texture features which correlated with FEV1 and FEV1/FVC (r=0.2-0.5; p<0.05) and yielded a classification accuracy of 85%. Conclusion: For the first time, we have employed hyperpolarized 3He MRI ventilation texture features and machine learning to identify COPD patients with accelerated decline in FEV1 with 84% accuracy.
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