BACKGROUND: 18F-FDG PET/CT has become an important tool in diagnosis of prosthetic vascular graft infections (PVGI). The aim of the study was to identify the patterns of vascular graft infection in 18F-FDG PET/CT. MATERIAL AND METHODS:The study was performed in 24 patients with vascular graft infection, in 17 patients implanted in an open surgery mode and in 7 patients by endovascular aortic repair (EVAR). Vascular prostheses were evaluated by two visual scales and semi-quantitative analysis with maximum standardized uptake values (SUV max). RESULTS:In the 3-point scale: 23 patients were in grade 1 and one patient was in grade 2. In the 5-point scale: 19 patients were in grade 5 with the highest activity in the focal area, 4 patients were in grade 4 and one patient in grade 3. The visual evaluation of 18F-FDG PET/CT study revealed that peri-graft high metabolic activity was associated with occurrence of morphological abnormalities (n = 21) like gas bubbles and peri-graft fluid retention or without abnormal CT findings (n = 3). The presence of the gas bubbles was linked to higher uptake of 18F-FDG (p < 0.01, SUVmax 11.81 ± 4.35 vs 7.36 ± 2.80, 15 vs 9 pts). In EVAR procedure, the highest metabolic activity was greater than in classical prosthesis (SUVmax 21.5 vs 13).CONCLUSIONS: 18F-FDG PET/CT is a very useful tool for assessment of vascular graft infections. CT findings like gas bubbles, or peri-graft fluid retention were associated with significantly higher glucose metabolism; however, in some cases without anatomic alterations, increased metabolic activity was the only sign of infection.
The detection of prostate cancer is an important challenge for medical personnel. To improve the medical system’s ability to process increasing numbers of oncological patients, demand for automation systems is growing. At the National Information Processing Institute, such systems are undergoing active development. In this work, the authors present the results of a pilot study whose goal is to analyze possible directions in the development of new, advanced deep learning systems using a high quality dataset that is currently in development.
Artificial intelligence (AI) in prostate MRI analysis shows great promise and impressive performance. A large number of studies present the usefulness of AI models in tasks such as prostate segmentation, lesion detection, and the classification and stratification of a cancer’s aggressiveness. This article presents a subjective critical review of AI in prostate MRI analysis. It discusses both the technology’s current state and its most recent advances, as well as its challenges. The article then presents opportunities in the context of ongoing research, which possesses the potential to reduce bias and to be applied in clinical settings.
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