METHODS: In this pictorial review, we present six different scenarios of using 18F-FDG PET-CT in the management of suspicious pulmonary nodule or mass. The advantages and limitations of 18F-FDG PET-CT and Herder model are discussed.
RESULTS: 18F-FDG PET-CT with risk assessment using Herder model provides added value in characterising indeterminate pulmonary nodules. Besides, 18F-FDG PET-CT is valuable to guide the site of biopsy and provide accurate staging of lung cancer.
CONCLUSION: To further improve its diagnostic accuracy, careful history taking, and CT morphological evaluation should be taken into consideration when interpreting 18FFDG PET-CT findings in patients with these nodules.
METHODS: We followed up 240 participants (112 cognitively unimpaired [CU], 78 amnestic mild cognitive impairment [aMCI], and 50 Alzheimer's disease (AD) dementia [ADD]) for 2 years from 9 referral centers in South Korea. Participants were assessed with neuropsychological tests and 18F-flutemetamol (FMM) positron emission tomography (PET). Ten regions (frontal, precuneus/posterior cingulate (PPC), lateral temporal, parietal, and striatum of each hemisphere) were visually examined in the FMM scan, and participants were divided into three groups: No-FMM, Focal-FMM (FMM uptake in 1-9 regions), and Diffuse-FMM. We used mixed-effects model to investigate the speed of cognitive decline in the Focal-FMM group according to the cognitive level, extent, and location of Aß involvement, in comparison with the No- or Diffuse-FMM group.
RESULTS: Forty-five of 240 (18.8%) individuals were categorized as Focal-FMM. The rate of cognitive decline in the Focal-FMM group was faster than the No-FMM group (especially in the CU and aMCI stage) and slower than the Diffuse-FMM group (in particular in the CU stage). Within the Focal-FMM group, participants with FMM uptake to a larger extent (7-9 regions) showed faster cognitive decline compared to those with uptake to a smaller extent (1-3 or 4-6 regions). The Focal-FMM group was found to have faster cognitive decline in comparison with the No-FMM when there was uptake in the PPC, striatum, and frontal cortex.
CONCLUSIONS: When predicting cognitive decline of patients with focal Aß deposition, the patients' cognitive level, extent, and location of the focal involvement are important.
MATERIALS AND METHODS: 18F-FDG PET/CT images of 14 healthy control (HC) subjects (MoCA score > 26 (mean+SD~ 26.93+0.92) with no clinical evidence of cognitive deficits or neurological disease) and 16 AD patients (MoCA ≤22 (mean+SD~18.6+9.28)) were pre-processed in SPM12 while using our developed Malaysian healthy control brain template. The AD patients were assessed for disease severity using ADAS-Cog neuropsychological test. KNE96 template was used for registration-induced deformation in comparison with the ICBM templates. All deformation fields were corrected using the Malaysian healthy control template. The images were then nonlinearly modified by DARTEL to segment grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to produce group-specific templates. Age, intracranial volume, MoCA score, and ADASCog score were used as variables in two sample t test between groups. The inference of our brain analysis was based on a corrected threshold of p<0.001 using Z-score threshold of 2.0, with a positive value above it as hypometabolic. The relationship between regional atrophy in GM and WM atrophy were analysed by comparing the means of cortical thinning between normal control and three AD stages in 15 clusters of ROI based on Z-score less than 2.0 as atrophied.
RESULTS: One-way ANOVA indicated that the means were equal for TIV, F(2,11) = 1.310, p=0.309, GMV, F(2,11) = 0.923, p=0.426, WMV, F(2,11) = 0.158, p=0.856 and CSF, F(2,11) = 1.495 p=0.266. Pearson correlations of GM, WM and CSF volume between HC and AD groups indicated the presence of brain atrophy in GM (p=-0.610, p<0.0001), WM (p=-0.178, p=0.034) and TIV (p=-0.374, p=0.042) but showed increased CSF volume (p=0.602, p<0.0001). Voxels analysis of the 18FFDG PET template revealed that GM atrophy differs significantly between healthy control and AD (p<0.0001). Zscore comparisons in the region of GM & WM were shown to distinguish AD patients from healthy controls at the prefrontal cortex and parahippocampal gyrus. The atrophy rate within each ROI is significantly different between groups (c2=35.9021, df=3, p<0.0001), Wilcoxon method test showed statistically significant differences were observed between Moderate vs. Mild AD (p<0.0001), Moderate AD vs. healthy control (p=0.0005), Mild AD vs. HC (p=0.0372) and Severe AD vs. Moderate AD (p<0.0001). The highest atrophy rate within each ROI between the median values ranked as follows severe AD vs. HC (p<0.0001) > mild AD vs. HC (p=0.0091) > severe AD vs. moderate AD (p=0.0143).
CONCLUSION: We recommend a reliable method in measuring the brain atrophy and locating the patterns of hypometabolism using a group-specific template registered to a quantitatively validated KNE96 group-specific template. The studied regions together with neuropsychological test approach is an effective method for the determination of AD severity in a Malaysian population.