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Recent advancements in Alzheimer's disease treatment have focused on the elimination of amyloid-beta (Aβ) plaque, a hallmark of the disease. Monoclonal antibodies such as lecanemab and donanemab can alter disease progression by binding to different forms of Aβ aggregates. However, these treatments raise concerns about adverse effects, particularly amyloid-related imaging abnormalities (ARIA). Careful assessment of safety, especially regarding ARIA, is crucial. ARIA results from treatment-related disruption of vascular integrity and increased vascular permeability, leading to the leakage of proteinaceous fluid (ARIA-E) and heme products (ARIA-H). ARIA-E indicates treatment-induced edema or sulcal effusion, while ARIA-H indicates treatment-induced microhemorrhage or superficial siderosis. The minimum recommended magnetic resonance imaging sequences for ARIA assessment are T2-FLAIR, T2* gradient echo (GRE), and diffusion-weighted imaging (DWI). T2-FLAIR and T2* GRE are necessary to detect ARIA-E and ARIA-H, respectively. DWI plays a role in differentiating ARIA-E from acute to subacute infarcts. Physicians, including radiologists, must be familiar with the imaging features of ARIA, the appropriate imaging protocol for the ARIA workup, and the reporting of findings in clinical practice. This review aims to describe the clinical and imaging features of ARIA and suggest points for the timely detection and monitoring of ARIA in clinical practice.
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http://dx.doi.org/10.3348/kjr.2024.0105 | DOI Listing |
Radiol Med
September 2025
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141, Milan, Italy.
Metastatic involvement (MB) of the breast from extramammary malignancies is rare, with an incidence of 0.09-1.3% of all breast malignancies.
View Article and Find Full Text PDFJ Neuromuscul Dis
September 2025
Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
Background: Electrical impedance myography (EIM) has been proposed as an efficient, non-invasive biomarker of muscle composition in facioscapulohumeral muscular dystrophy (FSHD).
Objective: We investigate whether EIM parameters are associated with muscle structure measured by magnetic resonance imaging (MRI), muscle histology, and transcriptomic analysis as well as strength at the individual leg muscle level.
Methods: We performed a multi-center cross-sectional study enrolling 33 patients with FSHD.
Neuroradiology
September 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Purpose: To develop and validate an integrated model based on MR high-resolution vessel wall imaging (HR-VWI) radiomics and clinical features to preoperatively assess periprocedural complications (PC) risk in patients with intracranial atherosclerotic disease (ICAD) undergoing percutaneous transluminal angioplasty and stenting (PTAS).
Methods: This multicenter retrospective study enrolled 601 PTAS patients (PC+, n = 84; PC -, n = 517) from three centers. Patients were divided into training (n = 336), validation (n = 144), and test (n = 121) cohorts.
J Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.
Technol Cancer Res Treat
September 2025
Department of Nephrology, Dongyang People's Hospital, Dongyang, China.
ObjectiveTo evaluate the diagnostic performance of a combined model incorporating ultrasound video-based radiomics features and clinical variables for distinguishing between benign and malignant breast lesions.MethodsA total of 346 patients (173 benign and 173 malignant) were retrospectively enrolled. Breast ultrasound videos were acquired and processed using semi-automatic segmentation in 3D Slicer.
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