Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in...

Our objective was to train machine-learning algorithms on hyperpolarized ${}^3\mathrm{He}$ magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s ( ${\mathrm{FEV}}_1$ ) across 3 years.

Hyperpolarized ${}^3\mathrm{He}$ MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis.

We evaluated 88 ex-smoker participants with $31\pm 7$ months follow-up data, 57 of whom (22 females/35 males, $70\pm 9$ years) had negligible changes in ${\mathrm{FEV}}_1$ and 31 participants (7 females/24 males, $68\pm 9$ years) with worsening ${\mathrm{FEV}}_1\ge 60\mathrm{mL}/\text{year}$ . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict ${\mathrm{FEV}}_1$ decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone.

For the first time, we have employed hyperpolarized ${}^3\mathrm{He}$ MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in ${\mathrm{FEV}}_1$ with 82% accuracy.