Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.

ABSTRACT:

Purpose

Our objective was to train machine-learning algorithms on hyperpolarized He3 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 ( FEV1 ) across 3 years.

Approach

Hyperpolarized He3 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 a

SUBMITTER: Sharma M 

PROVIDER: S-EPMC11259551 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature