Adapting an algorithm used to classify cancer tumours can help to diagnose Alzheimer’s disease at an earlier stage, according to a new study.
Published in the latest edition of * Communications Medicine*, the new approach, described as an “important step forward”, involves a magnetic resonance imaging (MRI) brain scan taken on a standard 1.5 Tesla machine.
Researchers at Imperial College London, UK, adapted the algorithm and divided the brain into 115 regions and allocated 660 different features, such as size, shape and texture, to assess each region.
They then trained the algorithm to identify where changes to these features could accurately predict the existence of Alzheimer’s disease.
The team used data from the Alzheimer’s Disease Neuroimaging Initiative to test their approach on brain scans from more than 400 patients with early and later stage Alzheimer’s, healthy controls and patients with other neurological conditions, including frontotemporal dementia and Parkinson’s disease.
It was also tested with data from more than 80 patients undergoing diagnostic tests for Alzheimer’s at Imperial College Healthcare NHS Trust.
In 98% of cases, the MRI-based machine learning system alone could accurately predict if the patient had Alzheimer’s disease or not and could also distinguish between early and late-stage Alzheimer’s with fairly high accuracy in 79% of patients.
Study leader Professor Eric Aboagye, from Imperial’s Department of Surgery and Cancer, said: “Currently no other simple and widely available methods can predict Alzheimer’s disease with this level of accuracy, so our research is an important step forward.
“Many patients who present with Alzheimer’s at memory clinics do also have other neurological conditions, but even within this group our system could pick out those patients who had Alzheimer’s from those who did not.
“Waiting for a diagnosis can be a horrible experience for patients and their families. If we could cut down the amount of time they have to wait, make diagnosis a simpler process, and reduce some of the uncertainty, that would help a great deal.
“Our new approach could also identify early-stage patients for clinical trials of new drug treatments or lifestyle changes, which is currently very hard to do.”
Using the adapted algorithm, changes were identified in areas of the brain that were not previously associated with Alzheimer’s disease, including the cerebellum and the ventral diencephalon.
The research team say this opens up potential new avenues for research into these areas and their links to Alzheimer’s disease.
Dr Paresh Malhotra, consultant neurologist at Imperial College Healthcare NHS Trust and researcher in Imperial’s Department of Brain Sciences, said: “Although neuroradiologists already interpret MRI scans to help diagnose Alzheimer’s, there are likely to be features of the scans that aren’t visible, even to specialists.
“Using an algorithm able to select texture and subtle structural features in the brain that are affected by Alzheimer’s could really enhance the information we can gain from standard imaging techniques.”
Inglese M et al. A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease. *Communications Medicine* 20 June 2022.
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