Objective: We aimed to determine whether NaF-PET/CT or FDG-PET/CT can detect abdominal aortic molecular calcification and inflammation in patients with rheumatoid arthritis (RA).
Methods: In this study, 18 RA patients (4 women, 14 men; mean age 56.0 ± 11.7) and 18 healthy controls (4 women, 14 men; mean age 55.8 ± 11.9) were included. The controls were matched to patients by sex and age (± 4 years). All subjects of this study underwent NaF-PET/CT scanning 90 min following the administration of NaF. FDG-PET/CT imaging was performed 180 min following intravenous FDG injection. Using OsiriX software, the global mean standardized uptake value (global SUVmean) in abdominal aorta was calculated for both FDG and NaF. The NaF SUVmean and FDG SUVmean were divided by the blood pool activity providing target-to-background ratios (TBR) namely, NaF-TBRmean and FDG-TBRmean. The CT calcium volume score was obtained using a growing region algorithm based on Hounsfield units.
Results: The average NaF-TBRmean score among RA patients was significantly greater than that of healthy controls (median 1.61; IQR 1.49-1.88 and median 1.40; IQR 1.23-1.52, P = 0.002). The average CT calcium volume score among RA patients was also significantly greater than that of healthy controls (median 1.96 cm; IQR 0.57-5.48 and median 0.004 cm; IQR 0.04-0.05, P < 0.001). There was no significant difference between the average FDG-TBRmean scores in the RA patients when compared to healthy controls (median 1.29; IQR 1.13-1.52 and median 1.29; IQR 1.13-1.52, respectively, P = 0.98).
Conclusion: Quantitative assessment with NaF-PET/CT identifies increased molecular calcification in the wall of the abdominal aorta among patients with RA as compared with healthy controls, while quantitative assessment with FDG-PET/CT did not identify a difference in aortic vessel wall FDG uptake between the RA and healthy control groups.
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http://dx.doi.org/10.1007/s12149-020-01463-w | DOI Listing |
Lymphology
January 2024
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Cancer Foundation of India, Kolkata, West Bengal, India.
Objective: The case-control study aims to identify the potential risk and protective factors contributing to breast cancer risk in the high-incidence Aizawl population and the low-incidence Agartala population, using age-specific prevalence data of established reproductive factors and body mass index (BMI) among healthy women.
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Asian Pac J Cancer Prev
January 2025
Parul Institute of Applied Sciences, Parul University, Vadodara, India.
Background: Breast cancer remains a significant global health challenge, requiring innovative therapeutic strategies. In silico methods, which leverage computational tools, offer a promising pathway for vaccine development. These methods facilitate antigen identification, epitope prediction, immune response modelling, and vaccine optimization, accelerating the design process.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Department of Biochemistry, Biotechnology Research Institute, High Throughput Molecular and Genetic laboratory, Center for Excellences for Advanced Sciences, National Research Centre, Dokki, Giza, Egypt.
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