Building the Bridge: Radiology’s Role in Alzheimer’s Research

Ana M Franceschi, MD, Chair of the American College of Radiology Neuroradiology Research Committee, contributed this piece.

With the recent FDA approvals of novel therapies for Alzheimer's disease (AD), clinical practices are expecting to see an increase in volume of patients who may be candidates for these therapies. Subsequently, imaging referrals, particularly MRI and PET scans, will occur as clinicians seek to determine eligibility for the therapies and ensure ongoing safety monitoring. As the clinical landscape continues to evolve, radiology departments and facilities will need to adapt to these expected increases of neuroradiology volumes without compromising image quality and patient safety. 

Clinical findings associated with AD therapies, particularly critical safety findings, should utilize standardized reporting to ensure consistent interpretability by treating clinicians. For example, early detection and accurate classification of Amyloid-Related Imaging Abnormalities (ARIA) are necessary for patient safety and clinical decision-making in AD care. 

ARIA is a known complication in patients receiving anti-amyloid therapies in clinical trials and has also been reported in real-world research registries like the Alzheimer’s Network for Treatment and Diagnostics (ALZ-NET). However, ALZ-NET researchers are finding that there are real-world ARIA reporting inconsistencies, prompting concern for how findings are interpreted or escalated. 

For this reason, it is imperative that radiologists are not only experts and educators of detection and appearance of ARIA, but champions for standardization of ARIA reporting with ARIA with Emerging AD Therapeutics templates. Radiologists, inclusive of neuroradiologists, and generalists in private practice and academic institutions, serve as drivers of systemic change and can lead to advancements in practice now. 

In addition, radiologists reading brain scans for patients with AD are highly encouraged to serve as ambassadors for their imaging facilities and register for the ALZ-NET Imaging Network. When an imaging facility participates in ALZ-NET, they become part of the community of radiologists who are defining best practices.

What are the benefits of joining the ALZ-NET Imaging Network? 

Participating imaging facilities assign an imaging staff member to upload DICOM studies for ALZ-NET participants in exchange for: 

• Collaboration Opportunities – Ability to collaborate and network with dementia practices, healthcare providers and research experts to increase professional recognition and utilization of your radiology services. 
  Exclusive Resources – Exposure to novel educational materials, scientific findings and work-flow optimization resources developed by ALZ-NET collaborators and partnering organizations (e.g. template radiology reports, ARIA detection).
  Access to AI Products – Discounted use of FDA-cleared AI products that aid in the clinical workflow of Alzheimer's disease diagnosis and treatment.  
  
  • icometrix's icobrain products - aiding with early detection, quantification and grading of AD and treatment response (e.g. ARIA). 
  • Cortechs.ai NeuroQuant products - intended for segmentation, quantification, and longitudinal tracking of lesions and brain structures from a set of MR sequences, including FLAIR and T2*GRE/SWI.
• Remuneration – $50 per DICOM study for facility’s time and effort with the image upload processes. 
• Access to Data – Streamlined access to structured local data and aggregated de-identified data collected by ALZ-NET. Used for research and/or data-driven insights for quality-of-care purposes. 

As novel therapies continue to emerge in the AD field, ALZ-NET will continue to promote the value of robust imaging datasets as a means of understanding the progression of disease and the benefit of the available therapies. A radiologist's role in AD science advancement is clear – they are architects of the bridge between real-world imaging and research that is essential for driving science-based clinical decision-making.