Hypointense signal lesion on susceptibility-weighted imaging as a potential indicator of vertebral artery dissection in medullary infarction.

Vertebral artery dissection (VAD) is often associated with medullary infarction; however, an underlying cause may be underestimated. This study aimed to assess the diagnostic potential of hypointense signal lesions along the arterial pathways using susceptibility-weighted imaging (SWI) as a feasible indicator of VAD in medullary infarction. A retrospective analysis was conducted using clinical data, brain magnetic resonance imaging, and angiography records of 79 patients diagnosed with medullary infarction between January 2014 and December 2021. Patients were categorized into an angiography-confirmed dissection group and a non-dissection group based on imaging findings. A new possible dissection group was identified using SWI, including cases with hypointense signals along the arteries without calcification or cardioembolism. We compared the clinical characteristics of the two groups before and after the addition of the hypointense signal as a marker of VAD. The angiography-confirmed dissection group included 12 patients (15%). Among patients lacking angiographic VAD evidence, 14 subjects displayed hypointense signals on SWI: nine patients along the vertebral artery and five subjects at the posterior inferior cerebellar artery without calcification or cardioembolism. The newly classified dissection group was younger, had a lower prevalence of diabetes mellitus and stroke history, and revealed increased headaches compared to the non-dissection group. Hypointense signal detection on SWI in medullary infarctions shows promise as a diagnostic indicator for VAD. Suspicion of VAD is needed when the hypointense signal on SWI is noted, and considering different treatment strategies with angiographic follow-up will be helpful.

Amyloid-Related Imaging Abnormalities in the Era of Anti-Amyloid Beta Monoclonal Antibodies for Alzheimer's Disease: Recent Updates on Clinical and Imaging Features and MRI Monitoring.

Recent advancements in Alzheimer's disease treatment have focused on the elimination of amyloid-beta (Aß) plaque, a hallmark of the disease. Monoclonal antibodies such as lecanemab and donanemab can alter disease progression by binding to different forms of Aß 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.

Knot-and-ear sign: a pathognomonic ultrasonographic feature of suture granuloma after thyroid surgery.

PURPOSE: This study investigated the clinical and ultrasonographic (US) findings of suture granulomas and recurrent tumors, and aimed to identify specific characteristics of suture granulomas through an experimental study. METHODS: This retrospective study included 20 pathologically confirmed suture granulomas and 40 recurrent tumors between January 2010 and December 2020. The clinical findings included suture material, surgery, and initial TNM stage. The US findings included shape, size, margin, echogenicity, heterogeneity, vascularity, and internal echogenic foci. The distribution, paired appearance, and "knot-and-ear" appearance of internal echogenic foci were assessed. An experiment using pork meat investigated the US configuration of suture knots. RESULTS: Eighteen patients with 20 suture granulomas (15 women; mean age, 52±13 years) and 37 patients with 40 recurrent tumors (24 women; 54±18 years) were included. Patients with suture granulomas exhibited earlier initial T and N stages than those with recurrent tumors. The knot-and-ear appearance, defined as an echogenic dot accompanied by two adjacent echogenic dots or lines based on experimental findings, was observed in 50% of suture granulomas, but not in recurrent tumors (P<0.001). Central internal echogenic foci (68.8%, P=0.023) and paired appearance (75.0%, P<0.001) were more frequent in suture granulomas. During follow-up, 94.1% of suture granulomas shrunk. CONCLUSION: The knot-and-ear appearance is a potential pathognomonic finding of suture granuloma, and central internal echogenic foci with paired appearance were typical US features. Clinically, suture granulomas showed early T and N stages and size reduction during follow-up. Understanding these features can prevent unnecessary biopsy or diagnostic surgery.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.