Korean J Radiol.  2014 Jun;15(3):386-396. 10.3348/kjr.2014.15.3.386.

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

Affiliations
  • 1Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea. jmgoo@plaza.snu.ac.kr
  • 2Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-744, Korea.
  • 3Department of Multimedia Engineering, Seoul Women's University, Seoul 139-774, Korea.
  • 4Department of Radiology, SMG-SNU Boramae Medical Center, Seoul 156-707, Korea.

Abstract


OBJECTIVE
To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD).
MATERIALS AND METHODS
Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater kappa statistics.
RESULTS
Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater kappa value was improved from moderate (kappa = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (kappa = 0.82; 95% CI, 0.79-0.85) with the CAC map.
CONCLUSION
Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.

Keyword

Computer-aided classification; Computed tomography; Chronic obstructive pulmonary disease; Regional ventilation; Xenon CT

MeSH Terms

Aged
Area Under Curve
Feasibility Studies
Female
Humans
Male
Middle Aged
Observer Variation
Pulmonary Disease, Chronic Obstructive/physiopathology/*radiography
Pulmonary Emphysema/physiopathology/radiography
*Respiration
Retrospective Studies
Tomography, X-Ray Computed/*methods
Xenon/*diagnostic use
Xenon
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