A novel MRI technique that tracks naturally occurring polymers in cartilage can lead to the diagnosis of early-stage osteoarthritis and may lead to better drug treatment and fewer joint replacement surgeries.
A novel MRI technique that tracks naturally occurring polymers in cartilage can lead to the diagnosis of early-stage osteoarthritis and may lead to better drug treatment and fewer joint replacement surgeries.
Researchers from New York University and Tel Aviv University have found that decreased concentrations of glycosaminoglycan (GAG) reliably predict the onset of degenerative joint disease in its earliest stages, before patients become symptomatic. GAG is recognized as a biomarker for both osteoarthritis and degenerative disc disease, a cause of lower back pain.
"By monitoring GAG, one can intervene much earlier in the disease progression, before gross tissue defects such as ruptures become visible," research scientist Alexej Jerschow, Ph.D., of the NYU School of Medicine, told Diagnostic Imaging. "Used preventively, it can also look at patients over time to see what treatments are effective."
Jerschow reported the findings jointly with Ravinder R. Regatte, Ph.D., at the 2008 American Chemical Society meeting Aug. 20.
Enhanced MRI can be used to detect osteoarthritis, but it does not directly assess GAG levels, Jerschow said. The research team’s chemical exchange-dependent saturation transfer method, or gagCEST, separates the protons and hydrogen content in GAG to produce a type of natural contrast agent, making it possible to monitor GAG and assess joint health in a clinical setting.
Osteoarthritis is usually not diagnosed until replacement surgery is the the only viable treatment. Early diagnosis with the gagCEST MR approach could help prevent or reduce permanent joint damage, especially with evidence that dietary supplements like glucosamine and chondroitin can halt further joint degeneration, Jerschow said.
"As we gain more insight into osteoarthritis with this and other methods, perhaps a full explanation of the mechanism and prevention of this disease can be found," he said.
-By Marjorie Preston
MRI-Based AI Radiomics Model Offers 'Robust' Prediction of Perineural Invasion in Prostate Cancer
July 26th 2024A model that combines MRI-based deep learning radiomics and clinical factors demonstrated an 84.8 percent ROC AUC and a 92.6 percent precision-recall AUC for predicting perineural invasion in prostate cancer cases.
Breast MRI Study Examines Common Factors with False Negatives and False Positives
July 24th 2024The absence of ipsilateral breast hypervascularity is three times more likely to be associated with false-negative findings on breast MRI and non-mass enhancement lesions have a 4.5-fold likelihood of being linked to false-positive results, according to new research.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.