title: Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework.
abstract: OBJECTIVE
  This study evaluated the performance of computed tomography (CT)-derived biomechanical based features of lung function and the presence and severity of chronic obstructive pulmonary disease (COPD). It performed well when compared to CT-derived density and textural features of lung function and the presence and severity of COPD.
  
  
  METHODS
  A total of 162 subjects (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages 0-4 and nonsmokers) subjects with CT scan performed at total lung capacity or expiration to functional residual capacity were evaluated. CT-derived biomechanical, density, and textural feature sets were compared to forced expiratory volume in 1 second (FEV1)%, FEV1/forced vital capacity, and total St. George's respiratory questionnaire scores. The ability of these feature sets to assess the presence and severity of COPD was also evaluated. Optimal features are selected by linear forward feature selection and the classification is done using k nearest neighbor learning algorithm.
  
  
  RESULTS
  The proposed biomechanical features showed good correlations with the pulmonary function tests and health status metrics. In COPD versus non-COPD classification, biomechanical feature set achieved an area under the curve (AUC) of 0.85 performing well in comparison to density (AUC = 0.83) and texture (AUC = 0.89) feature sets. Classifying the subjects into the severity of GOLD stage using biomechanical features (AUC = 0.81) performed better than the density- and texture-based feature sets, AUC = 0.76 and 0.73, respectively. The biomechanical features performed better alone than in combination with the other two feature sets.
  
  
  CONCLUSIONS
  This study shows the effectiveness of CT-derived biomechanical measures in the assessment of airflow obstruction and quality of life in subjects with COPD. CT-derived biomechanical features performed well in assessing the presence and severity of COPD.
authors.affiliation: Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
authors.forename: Sandeep
authors.initials: S
authors.lastname: Bodduluri
authors.forename: John D
authors.initials: JD
authors.lastname: Newell
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: Joseph M
authors.initials: JM
authors.lastname: Reinhardt
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D008297:Male; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012129:Respiratory Function Tests / Q000379:methods; D015656:Respiratory Mechanics; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*