%0 Journal Article
%T Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.
%A Sharma M
%A Kirby M
%A Fenster A
%A McCormack DG
%A Parraga G
%J J Med Imaging (Bellingham)
%V 11
%N 4
%D 2024 Jul
%M 39035052
暂无%R 10.1117/1.JMI.11.4.046001
%X <B>UNASSIGNED: </B>Our objective was to train machine-learning algorithms on hyperpolarized   He   3    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 (  FEV 1   ) across 3 years.<BR><B>UNASSIGNED: </B>Hyperpolarized   He   3    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.<BR><B>UNASSIGNED: </B>We evaluated 88 ex-smoker participants with 31 ± 7  months follow-up data, 57 of whom (22 females/35 males, 70 ± 9  years) had negligible changes in  FEV 1   and 31 participants (7 females/24 males, 68 ± 9  years) with worsening   FEV 1  ≥ 60    mL / year  . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict  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.<BR><B>UNASSIGNED: </B>For the first time, we have employed hyperpolarized   He   3    MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in  FEV 1   with 82% accuracy.