| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Courtois, A, et al.
(författare)
-
Positron Emission Tomography/Computed Tomography Predicts and Detects Complications After Endovascular Repair of Abdominal Aortic Aneurysms
- 2019
-
Ingår i: Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists. - : SAGE Publications. - 1545-1550. ; 26:4, s. 520-528
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To assess if aortic 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) could play a role in predicting complications after endovascular aneurysm repair (EVAR). Materials and Methods: This study involved 2 cohorts of men with abdominal aortic aneurysm treated by EVAR: those who underwent a PET/CT scan before EVAR (n=17) and those who had a PET/CT during follow-up (n=34). Uptake of FDG was measured as the standardized uptake value (SUV). D-dimer, a marker of fibrinolysis, was measured in blood drawn concomitantly with the PET/CT. Results: A significant uptake of FDG in the aneurysm wall was detected by PET/CT before EVAR in 6 of 17 patients. During the first year after EVAR, type II endoleaks developed in 5 of these FDG+ patients vs 3 of 11 FDG– patients (p=0.04). Two of the FDG+ patients had continued sac growth and required conversion to open repair. A significant association between sac growth rate, SUV, and the presence of endoleak was found in the 34 patients who underwent PET/CT after EVAR. Finally, D-dimer was significantly increased in patients with both endoleak and positive PET/CT in the post-EVAR group. Conclusion: This study suggests that the presence of FDG uptake in the aortic wall might be a useful tool to predict patients at high risk of developing post-EVAR complications.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Slart, Riemer H. J. A., et al.
(författare)
-
Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT
- 2021
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer. - 1619-7070 .- 1619-7089. ; 48:5, s. 1399-1413
-
Tidskriftsartikel (refereegranskat)abstract
- In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
|
|
