  The application of computer-vision algorithms in medical imaging has
increased rapidly in recent years. However, algorithm training is challenging
due to limited sample sizes, lack of labeled samples, as well as privacy
concerns regarding data sharing. To address these issues, we previously
developed (Bergen et al. 2022) a synthetic PET dataset for Head and Neck (H and
N) cancer using the temporal generative adversarial network (TGAN) architecture
and evaluated its performance segmenting lesions and identifying radiomics
features in synthesized images. In this work, a two-alternative forced-choice
(2AFC) observer study was performed to quantitatively evaluate the ability of
human observers to distinguish between real and synthesized oncological PET
images. In the study eight trained readers, including two board-certified
nuclear medicine physicians, read 170 real/synthetic image pairs presented as
2D-transaxial using a dedicated web app. For each image pair, the observer was
asked to identify the real image and input their confidence level with a
5-point Likert scale. P-values were computed using the binomial test and
Wilcoxon signed-rank test. A heat map was used to compare the response accuracy
distribution for the signed-rank test. Response accuracy for all observers
ranged from 36.2% [27.9-44.4] to 63.1% [54.8-71.3]. Six out of eight observers
did not identify the real image with statistical significance, indicating that
the synthetic dataset was reasonably representative of oncological PET images.
Overall, this study adds validity to the realism of our simulated H&N cancer
dataset, which may be implemented in the future to train AI algorithms while
favoring patient confidentiality and privacy protection.
