Automated lung volumetry from routine thoracic CT scans: how reliable is the result?
OBJECTIVE
Today, lung volumes can be easily calculated from chest computed tomography (CT) scans. Modern postprocessing workstations allow automated volume measurement of data sets acquired. However, there are challenges in the use of lung volume as an indicator of pulmonary disease when it is obtained from routine CT. Intra-individual variation and methodologic aspects have to be considered. Our goal was to assess the reliability of volumetric measurements in routine CT lung scans.


METHODS
Forty adult cancer patients whose lungs were unaffected by the disease underwent routine chest CT scans in 3-month intervals, resulting in a total number of 302 chest CT scans. Lung volume was calculated by automatic volumetry software. On average of 7.2 CT scans were successfully evaluable per patient (range 2-15). Intra-individual changes were assessed.


RESULTS
In the set of patients investigated, lung volume was approximately normally distributed, with a mean of 5283 cm(3) (standard deviation = 947 cm(3), skewness = -0.34, and curtosis = 0.16). Between different scans in one and the same patient the median intra-individual standard deviation in lung volume was 853 cm(3) (16% of the mean lung volume).


CONCLUSIONS
Automatic lung segmentation of routine chest CT scans allows a technically stable estimation of lung volume. However, substantial intra-individual variations have to be considered. A median intra-individual deviation of 16% in lung volume between different routine scans was found.
Department of Radiology, Charité Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, D-12203 Berlin, Germany. Electronic address: Matthias.Haas@charite.de.
Matthias
M
Haas
Department of Radiology, Charité Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, D-12203 Berlin, Germany.
Bernd
B
Hamm
Department of Radiology, Charité Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, D-12203 Berlin, Germany.
Stefan M
SM
Niehues
D000368:Aged; D000369:Aged, 80 and over; D003955:Diagnostic Tests, Routine / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D008176:Lung Volume Measurements / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
Lung functionality; computed tomography; lung volumetry; routine chest CT; thorax