Texture analysis may be used to extract quantitative information from hyperpolarized 3He MR ventilation images to help explain clinically-relevant outcomes and disease progression. We aimed to combine texture analysis with machine-learning to generate classification models for predicting worsening quality-of-life in ex-smokers with and without COPD. We identified six texture feature contributors, which outperformed standard imaging and clinical variables, with the top machine-learning model achieving a classification accuracy of 80.2% at predicting worsening quality-of-life within 2-3 years. These pilot results suggest that 3He MRI texture features may provide additional prognostic information to predict clinically-relevant changes in quality-of-life in ex-smokers.
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