Title: Amyloid DuAl-biomarker PeT for DEmEntia diAgnosis and subTyping (ADAPT DEfEAT)
Funder: Health Education England

Abstract: Background Dementia is a syndrome which affects over 850,000 people in the UK. Dementia is caused by a myriad of underlying diseases, or subtypes, resulting in a heterogenous patient group. Early diagnosis is essential to facilitate access to treatment and support, whilst determining the specific disease subtype and stage of an individual patient is crucial for personalised care. This information is also vital to inform the patient about the likely disease course, to empower them and their carers to make decisions about the future.Positron emission tomography (PET) is a sensitive technique for imaging biological changes in the brain through the injection and detection of targeted radiotracers. PET images of the concentration and distribution of amyloid-beta plaques (amyloid-PET), which are deposited in the early stages of the Alzheimer's Disease (AD) subtype, have been successfully used for disease diagnosis and staging. However, subtyping using amyloid-PET alone is limited to distinguishing between AD variants and Dementia with Lewy Bodies. Fluorodeoxyglucose (FDG) PET measures neuronal injury, and the patterns of the radiotracer uptake can be used to subtype a range of dementias. However, areas of abnormality may be subtle and difficult to distinguish from normal variation and healthy ageing, so computer aided diagnosis (CAD) is recommended to increase sensitivity and reduce diagnostic uncertainty. Combining the complementary information from both amyloid and FDG-PET could improve staging and subtyping, though current CAD tools cannot do this. Furthermore, acquiring both imaging modalities would require two separate tracer injections and hospital visits, resulting in increased inconvenience and radiation dose for the patient and an additional cost to the health service. Aims The aim of this project is to produce a CAD tool for the analysis of multi-modality PET data to support the clinician in dementia staging and subtyping. State-of-the-art machine learning techniques will be applied to provide individualised patient reports which identify areas of abnormality and estimate disease stage and subtype. Methods This project will utilise existing amyloid-PET datasets of both healthy older controls and patients with dementia. The recently developed 'dual-biomarker' method will be exploited to obtain amyloid-beta and FDG-like information from extended amyloid PET scans. This approach can improve the accuracy of the amyloid-beta quantification, as well as reducing the burden on both the patient and the health service compared to 2 PET scans. To highlight areas of abnormality within individual patient images statistical models of heterogeneity will be applied to the dual-biomarker PET data. This approach maintains sensitivity even in heterogeneous datasets, and also produces uncertainty estimates which are useful for interpretation. The Subtype and Stage Inference (SuStaIn) model, which finds the most likely ordering of disease progression, will also be applied to estimate both disease subtype and stage simultaneously with minimal assumptions imposed on the data. Impact The individualised patient reports will aid in clinical decision making by increasing the confidence and accuracy of disease staging and subtyping. This allows patients and their carers to be better informed about their disease status as well as empowering them to make decisions about the future.

Lay abstract: Background The term dementia is used to describe symptoms which occur because of declining brain function. The most common cause of dementia is Alzheimer's disease, but it can be caused by a variety of diseases. The specific disease someone has is called the subtype. The dementia subtype determines the symptoms experienced by the patient, the treatment options and support required, and how quickly the symptoms will progress. Accurate and timely dementia diagnosis and subtyping is essential to be able to provide personalised patient care. It also provides patients and their loved ones with information to help planning for the future.Currently disease subtyping is performed by asking the patient a series of questions to establish their symptoms and to exclude other causes. However, this can still leave the diagnosis uncertain so the patient may be sent for further testing, including brain scans. These scans can be performed using positron emission tomography (PET), which involves the injection of a tracer that shows up on the subsequent scan. One type of PET tracer can be used to measure changes in blood flow in the brain. The pattern of these changes can be used to diagnose the subtype and to measure the extent, or 'stage', of the disease. However, blood flow changes may not be clear cut in the early stages, and some disease subtypes may present similar patterns. This can make early, accurate, subtyping difficult.Amyloid-beta is a toxic protein which is always present in the Alzheimer's disease subtype. Amyloid-beta appears early on, up to 20 years before symptoms occur. Amyloid-beta targeting PET tracers can be used to measure the amount of amyloid-beta in the brain. Studies have shown that combining blood flow and amyloid-beta measurements from these 2 types of PET scan can improve subtype diagnosis. However, amyloid-beta PET is currently not recommended in government guidelines due to the additional cost. Performing both types of PET scan would also require two separate hospital visits, and two injections of a tracer which carries a small radiation risk. Aim The aim of this project is to develop a state-of-the-art artificial intelligence (AI) tool to analyse the two types of PET scans. AI can combine blood flow and amyloid-beta information and highlight subtle changes, helping clinicians determine the subtype and disease stage. The project will also take advantage of a new 'dual-biomarker' method, which produces the two types of PET scan from a single hospital visit and tracer injection. The AI tool combined with the dual-biomarker scan is designed to meet the urgent clinical need for increased confidence and accuracy in subtype diagnosis and staging, without greatly increasing the burden on the patient or the cost to the hospital. Design & Methods To develop the AI tool, large existing research datasets containing PET scans and test results will be used to learn the distinctive patterns shown in the PET images for different disease subtypes. The mathematical model within the AI tool can also learn how the patterns change over the course of the disease. This can be used to determine the disease stage. Patient & Public Involvement This project will be advised by a steering group made up of patients, carers, and members of the public who will advise on research priorities and the direction of the project throughout. There will also be an opportunity for patients, the public and clinicians to engage with the project through two workshops. Communication In addition to the workshops, results will be communicated to the scientific community through conferences and journal papers. Papers will also be freely available to the public, in addition to plain English explanations online.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL