Publications by authors named "J L Alberini"
Article Synopsis
- PET/CT is a diagnostic imaging tool gaining traction for identifying large-vessel vasculitis, particularly giant cell arteritis (GCA).
- It effectively highlights inflammation in large arteries like the aorta, demonstrating its frequent involvement and the risk of serious complications like aneurysms.
- Despite its benefits, challenges and uncertainties remain regarding the overall effectiveness of PET/CT in diagnosing and monitoring GCA, which this review aims to address.
Article Synopsis
- This study assessed the effectiveness of F-fluorocholine (FCH) PET/CT for locating hyperfunctioning parathyroid glands in patients with primary hyperparathyroidism (PHPT) before surgery.
- The research involved 80 patients who underwent FCH PET/CT, comparing the imaging results with histological findings and postoperative outcomes to evaluate the method's diagnostic performance.
- Results showed that FCH PET/CT had a sensitivity of 88% and a positive predictive value of 91%, indicating it is a valuable tool for pre-surgical localization of problematic parathyroid tissue in PHPT patients.*
Objectives: To evaluate the ability of FDG PET/CT, at diagnosis of giant cell arteritis (GCA) and during follow-up, to predict occurrence of relapse in large-vessel GCA (LV-GCA).
Methods: We conducted a retrospective study using the French Study Group for Large-Vessel Vasculitis (GEFA) network. Data from patients with LV-GCA diagnosed by PET/CT and who had PET/CT in the following year were collected.
Background: F-fluoroestradiol (FES) positron emission tomography (PET)/computed tomography (CT) is considered an accurate diagnostic tool to determine whole-body endocrine responsiveness. In the endocrine therapy (ET)-FES trial, we evaluated F-FES PET/CT as a predictive tool in estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC).
Patients And Methods: Eligible patients underwent an F-FES PET/CT at baseline.
In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or both. However, navigating the multitude of available architectures for automatic segmentation can be overwhelming, making it challenging to determine the appropriate type of architecture and tune the most crucial parameters during dataset optimisation. To address this problem, we examined and refined seven distinct architectures for segmenting the liver, as well as liver tumours, with a restricted training collection of 60 3D contrast-enhanced magnetic resonance images (CE-MRI) from the ATLAS dataset.
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