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. 2014 Aug 27;9(8):e105682.
doi: 10.1371/journal.pone.0105682. eCollection 2014.

A Semi-Automated Technique Determining the Liver Standardized Uptake Value Reference for Tumor Delineation in FDG PET-CT

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Free PMC article

A Semi-Automated Technique Determining the Liver Standardized Uptake Value Reference for Tumor Delineation in FDG PET-CT

Kenji Hirata et al. PLoS One. .
Free PMC article

Abstract

Background: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)-computed tomography (CT) has been an essential modality in oncology. We propose a semi-automated algorithm to objectively determine liver standardized uptake value (SUV), which is used as a threshold for tumor delineation.

Methods: A large spherical volume of interest (VOI) was placed manually to roughly enclose the right lobe (RL) of the liver. For each voxel in this VOI, a coefficient of variation of voxel values (CVv) was calculated for neighboring voxels within a radius of d/2. The voxel with the minimum CVv was then selected, where a 30-mm spherical VOI was placed at that voxel in accordance with PERCIST criteria. Two nuclear medicine physicians independently defined 30-mm VOIs manually on 124 studies in 62 patients to generate the standard values, against which the results from the new method were compared.

Results: The semi-automated method was successful in determining the liver SUV that was consistent between the two physicians in all the studies (d = 80 mm). The liver SUV threshold (mean +3 SD within 30-mm VOI) determined by the new semi-automated method (3.12±0.61) was not statistically different from those determined by the manual method (Physician-1: 3.14±0.58, Physician-2: 3.15±0.58). The semi-automated method produced tumor volumes that were not statistically different from those by experts' manual operation. Furthermore, the volume change in the two sequential studies had no statistical difference between semi-automated and manual methods.

Conclusions: Our semi-automated method could define the liver SUV robustly as the threshold value used for tumor volume measurements according to PERCIST. The method could avoid possible subjective bias of manual liver VOI placement and is thus expected to improve clinical performance of volume-based parameters for prediction of cancer treatment response.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. An illustration of the algorithm.
(a) The spherical VOIlarge confines the search area. (b) The spherical VOImedium is defined for each pixel in VOIlarge to calculate CVv ( =  standard deviation / mean). (c) VOI30 (sphere of 30-mm in diameter) is placed where CVv is smallest. The tumor delineation threshold is defined by the mean and SD within VOI30.
Figure 2
Figure 2. A representative case processed with the semi-automated algorithm.
A 150-mm spherical VOIlarge is manually placed to roughly enclose the right lobe of the liver (a, b). For each voxel within VOIlarge, a spherical VOImedium (e.g., 80 mm in diameter) is defined, and mean (c), SD (d), and coefficient of variation of voxel (CVv) (e) within VOImedium are calculated. A 30-mm VOI30 is placed where CVv is minimized (f, g). Image color scales are 0 to 4 SUV for (a–c, f, g), 0 to 1 SUV for (d), and 0 to 0.25 (unitless number) for (e).
Figure 3
Figure 3. To evaluate inter-study same-subject reproducibility of the VOI30 location, a cuboid region (red rectangle) was manually created to precisely contain the whole liver.
Location of VOI30 (black circle) was expressed as formula image in the liver coordinate system.
Figure 4
Figure 4. A representative case of two studies from the same patient.
(a, b) first study and (c, d) second study. The VOI30's defined using different size of VOImedium (blue: 40 mm, yellow: 60 mm, black: 80 mm, green: 100 mm, red: 120 mm) are drawn on transaxial slices (a, c) and maximum intensity projection images (b, d). The smaller VOImedium located VOI30 further from hepatic portal region with a larger distance between the first and second studies of the same patient than larger VOImedium's did.
Figure 5
Figure 5. Euclidian distance between VOI30's from two subsequent studies of the same patient by different VOImedium sizes.
Except the combination of 100-mm and 120-mm, all the combinations showed significant difference (P<0.001) after Holm's correction for multiple comparisons.
Figure 6
Figure 6. Bland-Altman plots of metabolic tumor volume.
A1 and A2 represent value from the semi-automated method operated by physician-1 and -2, respectively. M1 and M2 represent manually derived value by physician-1 and -2, respectively. (a) M1 vs. M2, (b) A1 vs. A2, (c) A1 vs. M1, and (d) A2 vs. M2 are compared. Solid lines represent mean difference and dashed lines represent mean ±2SD.
Figure 7
Figure 7. Bland-Altman plots of relative change of the metabolic tumor volume (MTV), calculated as [MTVsecond – MTVfirst] / MTVfirst×100 (%).
A1 and A2 represent value from the semi-automated method operated by physician-1 and -2, respectively. M1 and M2 represent manually derived value by physician-1 and -2, respectively. (a) M1 vs. M2, (b) A1 vs. A2, (c) A1 vs. M1, and (d) A2 vs. M2 are compared. Solid lines represent mean difference and dashed lines represent mean ±2SD.
Figure 8
Figure 8. Example images of CVv by different VOImedium's (a, d: 40 mm; b, e: 80 mm; c, f: 120 mm).
When 40-mm VOImedium was used (a, d), CVv seemed to be a non-convex function with many local minimums found in peripheral areas of the liver. When 80-mm VOImedium was used (b, e), CVv seemed to be a convex function. The minimum voxel existed in the right lobe of the liver. When 120-mm VOImedium was used (c, f), CVv seemed to be a convex function, but the minimum existed out of the liver. Image color scales are 0 to 0.25 for (a, d), 0 to 0.50 for (b, e), and 0 to 1.00 for (c, f).

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