CCCDTD5: Clinical role of neuroimaging and liquid biomarkers in patients with cognitive impairment

PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use

PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates in vivo. [18F]FDG and amyloid PET imaging have reached a high level of clinical validity and are since 2022 investigations that can be offered to patients in standard clinical care in most of Canada.This article will briefly review and summarize the current knowledge on these diagnostic tools, their integration into diagnostic algorithms as well as perspectives for future developments.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Decision making under uncertainty in the diagnosis and management of Alzheimer's Disease in primary care: A study protocol applying concepts from neuroeconomics

Background

The current management of patients with Dementia, primarily with Alzheimer's Disease (AD) is rapidly evolving. However, limited information is available about the current gaps and decision-making in primary care.

Objectives

To evaluate factors associated with gaps, risk preferences regarding diagnostic and therapeutic choices in the management of patients with AD by primary care physicians (PCP) from across Canada.

Methods

We propose a non-interventional, cross-sectional pilot study involving 120 primary care physicians referred from the College of Family Physicians of Canada to assess diagnostic and therapeutic decisions in the management of ten simulated AD-related case-scenarios commonly encountered in clinical practice. We initially describe the current landscape and gaps regarding diagnostic and therapeutic challenges in the management of patients with AD in primary care. Then, we provide concepts from behavioral economics and neuroeconomics applied to medical decision-making. Specifically, we include standardized tests to measure risk aversion, physicians' reactions to uncertainty, and questions related to risk preferences in different domains. Finally, we summarize the protocol to be implemented to address our goals. The primary study outcome is the proportion of participants that elect to defer initial investigations to the specialist and the associated factors. Secondary outcomes include the proportion of PCP willing to order cerebral spinal fluid studies, PET scans, or initiate treatment according to the simulated case-scenarios. The study will be conducted in English and French.

Conclusions

The study findings will contribute a better understanding of relevant factors associated with diagnostic and therapeutic decisions of PCP in the management of AD, identifying participant's preferences and evaluating the role of behavioral aspects such tolerance to uncertainty, aversion to ambiguity, and therapeutic inertia.