Chinese researchers find more unruptured cerebral aneurysms using magnetic resonance angiography.
Magnetic resonance angiography (MRA) with 3D time-of-flight (TOF) sequence may aid in detecting unruptured cerebral aneurysms, according to a study published in the Annals of Internal Medicine.
Researchers from Shanghai, China, undertook a cross-sectional study of adults, aged 35 to 75 years (mean age 53 years), to measure the prevalence of unruptured cerebral aneurysms (UCA) using 3D TOF MRA. The group was divided into four subgroups: those aged 35 to 44 years, 45 to 54 years, 55 to 64 years, and 65 to 75 years.
A total of 4,813 subjects (2,368 men and 2,445 women) from two randomly chosen districts in China participated in the study, which took place between June 2007 and June 2011. Three blinded observers interpreted the images, looking to identify the location and size of UCAs and to estimate the overall, age-specific, and sex-specific prevalence.
The observers detected 369 UCAs in 130 men (2.7 percent) and 206 women (4.3 percent), with 4,477 subjects having no evidence of an aneurysm. Most (90 percent) were less than 5 mm in diameter, although the mean diameter was larger in women than in men (3.7 mm vs. 3.2 mm).
Broken down by age group, the researchers found that the prevalence of UCAs was highest among those in the 55-to-64-year-old group. The most common area overall for UCAs was the internal carotid artery, accounting for 81 percent of the aneurysms (299). This was followed by 12 percent in the interior cerebral artery (46 total) and 11 percent in the anterior communicating artery (40 total).
The researchers noted that their higher number of detected aneurysms, compared with previous studies, may be related to their more specific MRI use, but they also wrote that their findings are limited by the ages of the subject (all were younger than 75 years old) and the subjects came from only two communities. Further research is needed, they concluded.
Can AI Enhance PET/MRI Assessment for Extraprostatic Tumor Extension in Patients with PCa?
December 17th 2024The use of an adjunctive machine learning model led to 17 and 21 percent improvements in the AUC and sensitivity rate, respectively, for PET/MRI in diagnosing extraprostatic tumor extension in patients with primary prostate cancer.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.