WASHINGTON: MRI can effectively and non-invasively screen patients for Alzheimer's disease or Frontotemporal Lobar Degeneration (FTLD), according to a new study.
Researchers from the Perelman School of Medicine at the University of Pennsylvania found that using an MRI-based algorithm effectively differentiated cases 75 percent of the time.
The non-invasive approach reported in this study can track disease progression over time more easily and cost-effectively than other tests, particularly in clinical trials testing new therapies.
Researchers used the MRIs to predict the ratio of two biomarkers for the diseases - the proteins total tau and beta-amyloid - in the cerebrospinal fluid. Cerebrospinal fluid analyses remain the most accurate method for predicting the disease cause, but requires a more invasive lumbar puncture.
"Using this novel method, we obtain a single biologically meaningful value from analyzing MRI data in this manner and then we can derive a probabilistic estimate of the likelihood of Alzheimer's or FTLD," said the study's lead author, Corey McMillan, PhD, of the Perelman School of Medicine and Frontotemporal Degeneration Center at the University of Pennsylvania.
Using the MRI prediction method was 75 percent accurate at identifying the correct diagnosis in both patients with pre-confirmed disease diagnoses and those with biomarker levels confirmed by lumbar punctures, which shows comparable overlap between accuracy of the MRI and lumbar puncture methods.
"For those remaining 25 percent of cases that are borderline, a lumbar puncture testing spinal fluid may provide a more accurate estimate of the pathological diagnosis," McMillan stated.
"Since this method yields a single biological value, it is possible to use MRI to screen patients for inclusion in clinical trials in a cost-effective manner and to provide an outcome measure that optimizes power in drug treatment trials," the researchers concluded.
The study was published in the latest issue of Neurology, the medical journal of the American Academy of Neurology.
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