Purpose To evaluate the diagnostic accuracy of fluorine-18-fluorodeoxyglucose PET/computed tomography (¹⁸F-FDG PET/CT) for lymph node (LN) metastasis and the prognostic significance of ¹⁸F-FDG PET/CT LN parameters in patients with resectable pancreatic cancer. Patients and methods Patients with resectable pancreatic cancer who underwent staging ¹⁸F-FDG PET/CT between May 2007 and September 2016 were retrospectively enrolled and analyzed through medical record and image re-evaluation. The diagnostic accuracy of ¹⁸F-FDG PET/CT in predicting LN metastasis was evaluated and compared with that of contrast-enhanced abdominal computed tomography (CECT). Prognostic variables, including LN parameters assessed by ¹⁸F-FDG PET/CT [standardized uptake value (SUV)_LN and LN/tumor SUV ratio], that affect disease-free survival (DFS) and overall survival (OS) were evaluated by regression analysis. Results When predicting LN metastasis, ¹⁸F-FDG PET/CT showed greater sensitivity, positive predictive value, negative predictive value, and accuracy than CECT. Among prognostic factors affecting DFS, PET-positive LN (P=0.008), and LN/tumor SUV ratio (P=0.003) were found to be significant by regression analysis. Among the variables affecting OS, lymphovascular invasion (P=0.018) and the LN/tumor SUV ratio (P=0.046) were found to be significant. Conclusion ¹⁸F-FDG PET/CT showed higher diagnostic accuracy in predicting LN metastasis than CECT in patients with resectable pancreatic cancer. Only the LN/tumor SUV ratio of ¹⁸F-FDG PET/CT was an independent prognostic variable in both DFS and OS.

This study reports a case of nodular fasciitis incidentally detected in a patient with papillary thyroid cancer. A 47-year-old woman underwent a total thyroidectomy and radioactive iodine therapy for papillary thyroid cancer. On a follow-up fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) scan after 12 months, a focal 18F-FDG-avid lesion was incidentally detected in the paraspinal muscle. It was well-enhanced on CT and magnetic resonance imaging, indicating metastasis. However, the lesion was not iodine avid on the 123I whole body scan, which favored benign etiology over metastasis from thyroid cancer. For pathological confirmation, surgical excision was performed and the paraspinal lesion was finally confirmed as nodular fasciitis. Therefore, it was suggested that nodular fasciitis may be included in the differential diagnosis of a 18F-FDG avid/iodine non-avid soft tissue lesion in patients with thyroid cancer. In such circumstances, 123I whole body scans may serve a role in non-invasive work-up, and prevent unnecessary surgical procedures.

Background and Objectives: ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PET_FDG) image can visualize neuronal injury of the brain in Alzheimer’s disease. Early-phase amyloid PET image is reported to be similar to PET_FDG image. This study aimed to generate PET_FDG images from ¹⁸F-florbetaben PET (PET_FBB) images using a generative adversarial network (GAN) and compare the generated PET_FDG (PET_GE-FDG) with real PET_FDG (PET_RE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PET_FDG and PET_FBB images at baseline were included. The paired PET_FDG and PET_FBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PET_FDG and PET_FBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PET_GE-FDG and PET_RE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student’s t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PET_GE-FDG and PET_RE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PET_GE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PET_GE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PET_FDG images from PET_FBB images using deep learning. The cycleGAN model generated PET_GE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PET_FDG images. These may provide additional information for the management of Alzheimer’s disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.

We report two cases of supra-scrotal vasitis incidentally detected in patients who had undergone mesh herniorrhaphy. PET/CT in patients undergoing lymphoma work-up and health check-up revealed hypermetabolic dilatation of vas deferens in the external iliac area. There were no symptoms, and blood test results did not indicate acute inflammation. Interestingly, both had undergone herniorrhaphy for inguinal hernia. Herniorrhaphy is reported to cause vasal complications such as obstruction or inflammation, although most are asymptomatic and probably under-reported. Chronic vasitis after herniorrhaphy may be a potential cause for false positive findings on 18F-FDG PET/CT in patients undergoing work-up for various oncological indications.

Background and Objectives: The optimal assessment of cognitive function, including the impact of education, is crucial in managing Alzheimer’s disease (AD). This study aimed to evaluate the role of cognitive reserve (CR), represented by the metabolic status of regions of the cerebral cortex, to evaluate cognitive decline considering the educational attainment of patients with AD. Materials and Methods: We used data from the Alzheimer’s Disease Neuroimaging Initiative database, and selected 124 patients who underwent both baseline F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben (FBB) positron emission tomography (PET) scans. Demographics, cognitive function variables (Clinical Dementia Rating—Sum of Boxes [CDR]; AD Assessment Scale 11/13 [ADAS11/13] Mini-Mental State Examination [MMSE]), and the average standardized uptake value ratio (SUVR) of cerebral cortex regions to those of the cerebellum were obtained from the data. The participants’ education level was divided into low and high education subgroups using four cut-offs of 12, 14, 16, and 18 years of educational attainment (G12, G14, G16, and G18, respectively). Demographic and cognitive function variables were compared between the two subgroups in each of the four groups, and their correlations with the SUVRs were evaluated. Results: There was no significant difference between the high and low education subgroups in each of the four groups, except for ADAS11/13 and MMSE in G14 and age in G16. The SUVRs of FDG PET (FDG_SUVR) were significantly correlated with CDR, ADAS11/13, and MMSE scores. FDG_SUVR showed different trajectories of neurodegeneration between the low and high education groups. Conclusions: FDG_SUVR correlated moderately but significantly with neuropsychological test results, without being influenced by education level. Therefore, FDG PET may reflect CR independent of education level, and therefore could be a reliable tool to evaluate cognitive decline in AD.