Using MRI to measure thalamic atrophy in patients with clinically isolated syndrome can help predict which patients will develop multiple sclerosis.
Magnetic resonance images that measure thalamic atrophy and increase in ventricular size can help predict progression of multiple sclerosis, according to a study published online in the journal Radiology.
Looking for an association between the development of thalamic atrophy and conversion to clinically definite multiple sclerosis (MS), researchers from the Buffalo Neuroimaging Analysis Center at the University of Buffalo in New York used contrast-enhanced MRI to assess 216 patients with clinically isolated syndrome (CIS), an initial, short-term neurological episode. CIS affects approximately 85 percent of people with MS. The MRIs were performed at first appointment and then again as follow-up at six months, one year, and two years.
Results showed that over two years, 92 of the patients (46.6 percent) converted to clinically definite MS. Decreases in thalamic volume and increase in lateral ventricle volumes were the only MRI measures independently associated with the development of MS.
“First, these results show that atrophy of the thalamus is associated with MS,” lead author Robert Zivadinov, MD, PhD, said in a release. “Second, they show that thalamic atrophy is a better predictor of clinically definite MS than accumulation of T2-weighted and contrast-enhanced lesions.”
Looking at these measurements may help identify high-risk patients in future clinical trials, because thalamic atrophy is detectable at an early stage, Zivadinov explained.
“The next step is to look at where the lesions develop over two years with respect to the location of the atrophy,” he concluded. “Thalamic atrophy cannot be explained entirely by accumulation of lesions; there must be an independent component that leads to loss of thalamus.”
Emerging AI Algorithm Shows Promise for Abbreviated Breast MRI in Multicenter Study
April 25th 2025An artificial intelligence algorithm for dynamic contrast-enhanced breast MRI offered a 93.9 percent AUC for breast cancer detection, and a 92.3 percent sensitivity in BI-RADS 3 cases, according to new research presented at the Society for Breast Imaging (SBI) conference.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
New bpMRI Study Suggests AI Offers Comparable Results to Radiologists for PCa Detection
April 15th 2025Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.