Semi-automated quantification of hepatic lesions in a phantom.
OBJECTIVE
Accurate measurement is crucial for the assessment of tumor dimensions to allow accurate evaluation of tumor response. Thus, the purpose of our study was to assess the accuracy of semi-automated RECIST and volumetric measurements of liver lesions in a liver phantom with different CT acquisition parameters.


METHODS
A phantom of the upper abdomen with 14 hepatic lesions of different sizes (diameter: 12.0-40.0 mm), densities (45/180 HU at 120 kV), or alignment (vertical/transverse) was scanned with a 16-slice multidetector row computed tomography using varying tube currents (40/60/80/100/120/165mAs eff), reconstruction kernels (Siemens B20/30/40/50/70s), or slice thicknesses (1/2/3/4/5 mm). Longest axial diameter and volume of the 14 lesions were quantified using a semi-automated software tool (SyngoOncology, Siemens Medical Solutions, Forchheim, Germany) and compared with the known real longest axial diameter and volume values of the lesions. Absolute percentage errors (APE) were calculated. Degree of agreement in longest axial diameter and volume between software and real measurements was represented graphically in Bland-Altman plots and by corresponding concordance correlation coefficient.


RESULTS
At standard soft tissue reconstruction kernel (Siemens B30s) and slice thickness (3 mm) mean absolute percentage error APE (concordance correlation coefficients) ranged between 6.93 and 14.27 (0.96 and 0.99) for longest axial diameter and between 4.98 and 10.85 (0.99 and 1.00) for volume. At varying reconstruction kernels, APE values (concordance correlation coefficients) ranged between 7.92 and 8.31 (0.98 and 0.99) for longest axial diameter and between 4.95 and 6.93 (1.00) for volume. Applying different slice sections APE values (concordance correlation coefficients) differed from 6.54 to 11.82 (0.97 and 0.99) for longest axial diameter and from 6.93 to 9.17 (1.00) for volume.


CONCLUSIONS
Software quantification of longest axial diameter and volume of hepatic lesions in a phantom demonstrated a high correlation and accuracy under varying multidetector row computed tomography parameter.
Keil
Sebastian
S
Department of Diagnostic Radiology, University Hospital, RWTH Aachen University, Aachen, Germany. keil@rad.rwth-aachen.de
Plumhans
Cedric
C
Behrendt
Florian F
FF
Stanzel
Sven
S
Suehling
Michael
M
Mühlenbruch
Georg
G
Mahnken
Andreas H
AH
Günther
Rolf W
RW
Das
Marco
M
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*