Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values.
Characterisation and quantification of emphysema are necessary for planning of local treatment and monitoring. Sensitive, easy to measure, and stable parameters have to be established and their relation to the well-known pulmonary function testing (PFT) has to be investigated. A retrospective analysis of 221 nonenhanced thin-section MDCT with a corresponding PFT was carried out, with a subgroup analysis in 102 COPD stage III+IV, 44 COPD stage 0, and 33 investigations into interstitial lung disease (ILD). The in-house YACTA software was used for automatic quantification of lung and emphysema volume [l], emphysema index, mean lung density (MLD [HU]) and 15(th) percentile [HU]. CT-derived lung volume is significantly smaller in ILD (3.8) and larger in COPD (7.2) than in controls (5.9, p < 0.0001). Emphysema volume and index are significantly higher in COPD than in controls (3.2 vs. 0.5, p < 0.0001, 45% vs. 8%, p < 0.0001). MLD and 15(th) percentile are significantly smaller in COPD (-877/-985, p < 0.0001) and significantly higher in ILD (-777, p < 0.0006/-914, p < 0.0001) than in controls (-829/-935). A relevant amount of COPD patients apparently do not suffer from emphysema, while controls who do not fulfil PFT criteria for COPD also demonstrate CT features of emphysema. Automatic quantification of thin-section CT delivers convincing parameters and ranges that are able to differentiate among emphysema, control and ILD. An emphysema index of lower 20%, MLD higher than -850, and 15(th) percentile lower than -950 might be regarded as normal (thin-section, nonenhanced, B40, YACTA). These ranges might be helpful in the judgement of individual measures.
Heussel
C P
CP
Thoraxklinik Heidelberg, University Hospital Heidelberg, Amalienstrasse 5, 69126, Heidelberg, Germany. heussel@uni-heidelberg.de
Herth
F J F
FJ
Kappes
J
J
Hantusch
R
R
Hartlieb
S
S
Weinheimer
O
O
Kauczor
H U
HU
Eberhardt
R
R
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011656:Pulmonary Emphysema / Q000175:diagnosis*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012129:Respiratory Function Tests; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*