Lecanemab and donanemab have received full U.S. Food and Drug Administration (FDA) approval, and subsequently, lecanemab has been approved by the Korean FDA and it has recently entered commercial use in Korea. This has increased interest in anti-amyloid immunotherapy for Alzheimer’s disease. Anti-amyloid immunotherapy has shown potential to modify the progression of the disease by specifically binding to amyloid β, a key pathological product in Alzheimer’s disease, and eliminating accumulated amyloid plaques in the brain. However, this treatment can be accompanied by a side-effect, amyloid-related imaging abnormalities (ARIA), which requires periodic monitoring by MRI. It is crucial to detect ARIA and accurately assess the severity by radiology. The role of the radiologist is important in this context, requiring proficiency in basic knowledge of ARIA, and in diagnosing/evaluating ARIA. This review aims to comprehensively cover aspects of ARIA, including its definition, pathophysiology, incidence, risk factors, assessment of severity by radiology, differential diagnosis, and management.

Lecanemab and donanemab have received full U.S. Food and Drug Administration (FDA) approval, and subsequently, lecanemab has been approved by the Korean FDA and it has recently entered commercial use in Korea. This has increased interest in anti-amyloid immunotherapy for Alzheimer’s disease. Anti-amyloid immunotherapy has shown potential to modify the progression of the disease by specifically binding to amyloid β, a key pathological product in Alzheimer’s disease, and eliminating accumulated amyloid plaques in the brain. However, this treatment can be accompanied by a side-effect, amyloid-related imaging abnormalities (ARIA), which requires periodic monitoring by MRI. It is crucial to detect ARIA and accurately assess the severity by radiology. The role of the radiologist is important in this context, requiring proficiency in basic knowledge of ARIA, and in diagnosing/evaluating ARIA. This review aims to comprehensively cover aspects of ARIA, including its definition, pathophysiology, incidence, risk factors, assessment of severity by radiology, differential diagnosis, and management.

Lecanemab and donanemab have received full U.S. Food and Drug Administration (FDA) approval, and subsequently, lecanemab has been approved by the Korean FDA and it has recently entered commercial use in Korea. This has increased interest in anti-amyloid immunotherapy for Alzheimer’s disease. Anti-amyloid immunotherapy has shown potential to modify the progression of the disease by specifically binding to amyloid β, a key pathological product in Alzheimer’s disease, and eliminating accumulated amyloid plaques in the brain. However, this treatment can be accompanied by a side-effect, amyloid-related imaging abnormalities (ARIA), which requires periodic monitoring by MRI. It is crucial to detect ARIA and accurately assess the severity by radiology. The role of the radiologist is important in this context, requiring proficiency in basic knowledge of ARIA, and in diagnosing/evaluating ARIA. This review aims to comprehensively cover aspects of ARIA, including its definition, pathophysiology, incidence, risk factors, assessment of severity by radiology, differential diagnosis, and management.

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 treatmentrelated 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.

The coronavirus disease-19 (COVID-19) pandemic leaded to inevitable expeditious vaccine rollout without sufficient safety profile. Especially, severe acute respiratory syndrome coronavirus 2 infection has known to induce overreacted immune responses such as releasing of proteinase-3 and myeloperoxidase (MPO) by neutrophil. This overreacted immune response leads to the concern of the development of autoimmune diseases after COVID-19 vaccination. We report the case of de novo MPO-associated systemic vasculitis involving central nervous system following heterogeneous mRNA1273 COVID-19 booster vaccination.

Objective: We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging.Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. Materials and Methods: A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. Results: Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. Conclusion The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.

In aging societies, incidences of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are increasing. Neurodegenerative diseases are bringing main challenges to the healthcare system in today’s world. Analyzing characteristic imaging patterns of patients with neurodegenerative diseases is important. Since objective and reliable imaging assessments and precise analyses can lead to early diagnosis of neurodegenerative diseases, imaging patterns are being increasingly investigated. Artificial intelligence (AI) analyzing brain MRI has been applied to neurodegenerative diseases, providing added value in early diagnosis. MRI-based AI software has been developed and studied worldwide, with some AI-based software already being used in actual clinical care. Currently, there are MRI-based volumetry and segmentation software available. There is also an unmet demand for the application of AI in neurodegenerative diseases. Here, we review current status and unmet needs for application of AI in neurodegenerative diseases. We also discuss current limitations of AI, suggestion for AI-based software, and how it can be clinically applied in the future.