CT angiogram monitoring of patients with cerebral aneurysms helps identify if and when the aneurysms grow, increasing the risk of rupture.
Monitoring cerebral aneurysms with CT angiogram permits physicians to watch for lesion growth regardless of the original size, allowing for quick intervention if necessary, according to a study published online in the journal Radiology.
Current guidelines suggest that known aneurysms that are smaller than seven millimeters do not need to be monitored with imaging because they are considered to be at low risk for rupturing. To assess if this recommendation was valid, researchers from the David Geffen School of Medicine at the University of California, Los Angeles, sought to identify the risk factors for cerebral aneurysm ruptures.
The researchers, led by J. Pablo Villablanca, MD, chief of diagnostic neuroradiology, studied 165 asymptomatic patients (132 women, 33 men) who had been discovered to have cerebral aneurysms either incidentally or during a baseline study. A total of 258 aneurysms were identified. The patients underwent CT angiography every six or 12 months for a mean of 2.24 years.
The images showed growth in 46 of all intracranial aneurysms in 38 patients. Three of the 39 growing saccular aneurysms ruptured, and of those, all were smaller than seven millimeters in size at study entry.
Compared to the aneurysms that stayed the same size, the 46 growing aneurysms were associated with a 12-fold higher risk of rupture. The researchers calculated the risk of rupture for growing aneurysms at 2.4 percent per patient-year versus 0.2 percent for aneurysms that did not grow.
“The positive association between aneurysm growth, aneurysm size, and cigarette smoking suggests that the combination of these factors are associated with an increased risk of rupture and may influence the need for therapeutic intervention,” Villablanca said in a release.
Photon-Counting Computed Tomography: Eleven Takeaways from a New Literature Review
May 27th 2025In a review of 155 studies, researchers examined the capabilities of photon-counting computed tomography (PCCT) for enhanced accuracy, tissue characterization, artifact reduction and reduced radiation dosing across thoracic, abdominal, and cardiothoracic imaging applications.
Can AI Predict Future Lung Cancer Risk from a Single CT Scan?
May 19th 2025In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.
Can Emerging AI Software Offer Detection of CAD on CCTA on Par with Radiologists?
May 14th 2025In a study involving over 1,000 patients who had coronary computed tomography angiography (CCTA) exams, AI software demonstrated a 90 percent AUC for assessments of cases > CAD-RADS 3 and 4A and had a 98 percent NPV for obstructive coronary artery disease.