Rationale and Objectives
The purpose of this study was to compare tumor volume in a VX2 rabbit model as calculated using semiautomatic tumor segmentation from C-arm cone-beam computed tomography (CBCT) and multidetector computed tomography (MDCT) to the actual tumor volume.
Materials and Methods
Twenty VX2 tumors in 20 adult male New Zealand rabbits (one tumor per rabbit) were imaged with CBCT (using an intra-arterial contrast medium injection) and MDCT (using an intravenous contrast injection). All tumor volumes were measured using semiautomatic three-dimensional volumetric segmentation software. The software uses a region-growing method using non-Euclidean radial basis functions. After imaging, the tumors were excised for pathologic volume measurement. The imaging-based tumor volume measurements were compared to the pathologic volumes using linear regression, with Pearson’s test, and correlated using Bland-Altman analysis.
Results
Average tumor volumes were 3.5 ± 1.6 cm 3 (range, 1.4–7.2 cm 3 ) on pathology, 3.8 ± 1.6 cm 3 (range, 1.3–7.3 cm 3 ) on CBCT, and 3.9 ± 1.6 (range, 1.8–7.5 cm 3 ) on MDCT ( P < .001). A strong correlation between volumes on pathology and CBCT and also with MDCT was observed (Pearson’s correlation coefficient = 0.993 and 0.996, P < .001, for CBCT and MDCT, respectively). Bland-Altman analysis showed that MDCT tended to overestimate tumor volume, and there was stronger agreement for tumor volume between CBCT and pathology than with MDCT, possibly because of the intra-arterial contrast injection.
Conclusions
Tumor volume as measured using semiautomatic tumor segmentation software showed a strong correlation with the “real volume” measured on pathology. The segmentation software on CBCT and MDCT can be a useful tool for volumetric hepatic tumor assessment.
A change in tumor volume as a response to local therapy such as transcatheter arterial chemoembolization is a prognostic indicator of therapeutic success. Tumor size is the only component of the Response Evaluation Criteria in Solid Tumors (RECIST) . First described in 2000, RECIST is based on tumor diameter measurement, in which the longest diameter of a given target lesion, or the sum of the longest diameters for a set of target lesions, is measured and compared before and after chemoembolization on cross-sectional imaging (either computed tomography or magnetic resonance imaging. It is a one-dimensional measurement that often poorly represents true tumor response after chemoembolization and is subject to high interobserver variability . Although RECIST was appropriate at the time of its introduction, the simplicity of RECIST now makes insufficient use of the sophisticated advances in modern imaging units. With the advent of multidetector computed tomography (MDCT) and improved detectors, the ability to assess tumor volume using three-dimensional (3D) metrics has become much more feasible. Furthermore, with the advent of C-arm cone-beam computed tomography (CBCT), tumors can be assessed during the procedure for planning or for efficacy of treatment . However, before volume-based metrics can supplant RECIST, these methods must be shown to be accurate and precise. This was recognized in version 1.1 of RECIST (released in 2009): the importance of studying volumetric anatomic assessment in greater detail is necessary before anatomic unidimensional assessment of tumor burden can be abandoned .
The purpose of this study was to evaluate the accuracy of semiautomatic tumor segmentation software with CBCT and MDCT and compare these measurements to pathologic volume-based measurements in a VX2 rabbit hepatic tumor model.
Materials and methods
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Animals
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Anesthesia
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MDCT
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Drug-eluting Bead Transarterial Chemoembolization (DEB-TACE) Procedure
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C-arm CBCT
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Animal Sacrifice, Histology, and Tumor Volume Measurement
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Semiautomatic Tumor Segmentation
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Statistical Analysis
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Results
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Table 1
Tumor Volume Measurements on Pathology, CBCT, and MDCT
Rabbit Pathology Volume Tumor Shape Diameter A (cm) Diameter B (cm) Volume (cm 3 ) CBCT (cm 3 ) MDCT (cm 3 ) 1 Prolate 2.2 1.1 2.79 2.92 3.21 1 ∗ 2.3 1 2.77 2.96 3.22 2 Prolate 1.7 2.3 3.48 3.43 3.61 2 ∗ 1.7 2.2 3.33 3.36 3.63 3 Prolate 1.9 0.8 1.51 1.58 2.06 3 ∗ 1.9 1 1.89 2.1 2.08 4 Prolate 2.2 2.8 7.09 7.15 7.46 4 ∗ 2.3 2.6 7.20 7.26 7.53 5 Prolate 1.4 2.7 2.77 2.98 3.32 5 ∗ 1.5 2.5 2.94 3.23 3.26 6 Oblate 1.3 3.1 6.54 6.63 6.95 6 ∗ 1.2 3.2 6.43 6.78 6.94 7 Oblate 1.6 1.3 1.42 1.32 1.76 7 ∗ 1.6 1.4 1.64 1.89 2.05 8 Oblate 1.8 1.6 2.41 2.63 2.81 8 ∗ 1.7 1.7 2.57 3.04 2.93 9 Prolate 1.8 0.8 1.36 1.73 1.79 9 ∗ 1.8 1 1.70 1.75 1.81 10 Prolate 2.5 1.3 4.25 4.36 4.43 10 ∗ 2.2 1.5 3.80 4.43 4.4 11 Oblate 1.8 1.4 1.85 2.17 2.12 11 ∗ 1.7 1.4 1.74 2.13 2.13 12 Prolate 1.6 2 2.68 3.11 3.18 12 ∗ 1.6 2.1 2.81 3.12 3.13 13 Prolate 2.1 1.2 2.77 3.22 3.01 13 ∗ 2.2 1.1 2.79 3.18 2.99 14 Prolate 1.9 2.1 3.97 4.34 4.42 14 ∗ 1.8 2.1 3.56 4.33 4.29 15 Oblate 1.6 2.2 4.05 4.52 4.51 15 ∗ 1.8 2.1 4.16 4.55 4.48 16 Oblate 1.1 2.4 3.32 3.65 3.65 16 ∗ 1.3 2.2 3.29 3.67 3.63 17 Prolate 2.4 1.8 5.43 5.72 5.81 17 ∗ 2.3 1.9 5.26 5.78 5.82 18 Oblate 1.7 2.3 4.71 5.09 5.12 18 ∗ 1.9 2.2 4.82 5.08 5.14 19 Prolate 2.7 1.4 5.34 5.38 5.5 19 ∗ 2.6 1.4 4.95 5.39 5.42 20 Prolate 2.1 1.2 2.77 3.08 3.17 20 ∗ 2.1 1.3 3.00 3.11 3.13
CBCT, cone-beam computed tomography; MDCT, multidetector computed tomography.
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Table 2
Predictive Performance of MDCT and CBCT Using the Sheiner and Beal Method
Variable CBCT (cm 3 ) MDCT (cm 3 ) Precision 0.674 0.321 Bias 0.368 0.275 Difference in precision (95% confidence interval) ∗ 0.193–0.410
CBCT, cone-beam computed tomography; MDCT, multidetector computed tomography.
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Table 3
Intraobserver Measurement Reproducibility Results, Showing Good Reproducibility of Tumor Volume Measurement by Each Method Used
Variable Pathology CBCT MDCT Average difference between two measurement (95% CI) 0.19 (0.02–0.45) 0.12 (0.01–0.57) 0.05 (0.01–0.29) Intraclass correlation coefficient (95% CI) 0.934 (0.922–0.967) 0.951 (0.938–0.987) 0.958 (0.952–0.963)
CBCT, cone-beam computed tomography; CI, confidence interval; MDCT, multidetector computed tomography.
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Discussion
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Conclusions
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