Imaging-based visual scores indicate probable Alzheimer’s disease with 91% accuracy

A visual score based on entorhinal cortex atrophy (ERICA) can accurately indicate probable Alzheimer’s disease, according to a new study published in Radiology.

A group of researchers from University Hospital Bonn in Bonn, Germany, and the German Center for Neurodegenerative Diseases, also in Bonn, performed a retrospective study by establishing an ERICA visual score for 48 consecutive patients. Twenty patients had Alzheimer’s, with a mean age of 73 years old, and 28 patients, with a mean age of 65 years old, were control subjects. All patients underwent MR imaging at the same institution between 2009 and 2016.

“The entorhinal cortex was evaluated in total from the level of the uncus-amygdala complex anteriorly to the dorsal hippocampus posteriorly … on the basis of established MR imaging protocols,” wrote lead author Alina Jurcoane, MD, PhD, and colleagues. To create reproducible results, we defined the ERICA score at the level of the mammillary bodies.”

ERICA scores were either a 0 for normal volume of the entorhinal cortex and parahippocampal gyrus, a 1 for mild atrophy, a 2 for moderate atrophy or a 3 for pronounced atrophy and “a wide cleft between entorhinal cortex and the cerebellar tentorium.”

Overall, patients with Alzheimer’s disease were found to have higher ERICA scores as well as higher medial temporal lobe atrophy (MTA) scores. An ERICA score of 2 or 3 had an overall diagnostic accuracy of 91 percent, higher than MTA score accuracy (74 percent). ERICA scores had a sensitivity of 83 percent and a specificity of 98 percent.

“The ERICA score can be used to evaluate atrophy of the entorhinal cortex in clinical routine, where an ERICA score of 2 or greater with a tentorial cleft sign indicates probable Alzheimer’s with high accuracy,” the authors wrote.

Michael Walter
Michael Walter, Managing Editor

Michael has more than 16 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.

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