Computer-aided classification of visual ventilation patterns in patients with chronic obstructive pulmonary disease at two-phase xenon-enhanced CT.
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).


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 κ 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 κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map.


CONCLUSIONS
Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea.
Soon Ho
SH
Yoon
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea. ; Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-744, Korea.
Jin Mo
JM
Goo
Department of Multimedia Engineering, Seoul Women's University, Seoul 139-774, Korea.
Julip
J
Jung
Department of Multimedia Engineering, Seoul Women's University, Seoul 139-774, Korea.
Helen
H
Hong
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea.
Eun Ah
EA
Park
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea.
Chang Hyun
CH
Lee
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea. ; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul 156-707, Korea.
Youkyung
Y
Lee
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea. ; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul 156-707, Korea.
Kwang Nam
KN
Jin
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea.
Ji Yung
JY
Choo
Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea.
Nyoung Keun
NK
Lee
D000368:Aged; D019540:Area Under Curve; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging* / Q000503:physiopathology; D011656:Pulmonary Emphysema / Q000000981:diagnostic imaging / Q000503:physiopathology; D012119:Respiration; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014978:Xenon
D014978:Xenon
Chronic obstructive pulmonary disease; Computed tomography; Computer-aided classification; Regional ventilation; Xenon CT