Hyperpolarized gas magnetic resonance imaging texture analysis and machine learning to explain accelerated lung function decline in ex-smokers with and without COPD

Abstract

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.