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

Kenji Hirata, Kentaro Kobayashi, Koon-Pong Wong, Osamu Manabe, Andrew Surmak, Nagara Tamaki and Sung-Cheng Huang

PLOS ONE, 2014, vol. 9, issue 8, 1-11

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0105682

DOI: 10.1371/journal.pone.0105682

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