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

Maksym Sharma; Andrew Westcott; David G. McCormack; Grace Parraga

doi:10.1117/12.2580451

14 February 2021 Hyperpolarized gas magnetic resonance imaging texture analysis and machine learning to explain accelerated lung function decline in ex-smokers with and without COPD

Maksym Sharma, Andrew Westcott, David G. McCormack, Grace Parraga

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000E (2021) https://doi.org/10.1117/12.2580451
Event: SPIE Medical Imaging, 2021, Online Only

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.

Conference Presentation

Citation Download Citation

Maksym Sharma, Andrew Westcott, David G. McCormack, and Grace Parraga "Hyperpolarized gas magnetic resonance imaging texture analysis and machine learning to explain accelerated lung function decline in ex-smokers with and without COPD", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000E (14 February 2021); https://doi.org/10.1117/12.2580451

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available