Brain images from FDG-PET can help detect Alzheimer’s disease in patients presenting with focal onset dementias.
Images of the brain using FDG-PET can accurately detect Alzheimer's disease in patients presenting with primary progressive aphasia or corticobasal syndrome as focal onset dementias, according to a study published in The Journal of Nuclear Medicine.
Authors from the United States, Japan, and Australia sought to determine the accuracy of FDG-PET metabolic imaging when detecting Alzheimer’s disease among patients with primary progressive aphasia or corticobasal syndrome.
A total of 94 subjects participated in the study, including subjects who were diagnosed with:
• Logopenic aphasia, 19 subjects
• Non-fluent aphasia, 16 subjects
• Semantic aphasia, 13 subjects[[{"type":"media","view_mode":"media_crop","fid":"41084","attributes":{"alt":"PET CT","class":"media-image media-image-right","id":"media_crop_6997834934021","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"4285","media_crop_rotate":"0","media_crop_scale_h":"0","media_crop_scale_w":"0","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","style":"height: 113px; width: 151px; border-width: 0px; border-style: solid; margin: 1px; float: right;","title":" ","typeof":"foaf:Image"}}]]
• Corticobasal syndrome, 14 subjects
• Alzheimer's disease, 24 subjects
All underwent F18-FDG metabolic and C11-PiB amyloid PET brain imaging. The FDG-PET scans that were read with Neurostat 3D-SSP software displays were classified as Alzheimer's disease or other by readers blind to the clinical assessments and PiB-PET results.
The results showed 84% accuracy for subjects who were diagnosed with Alzheimer’s disease based on FDG-PET results. However, diagnoses based on clinical assessment resulted in only 65% conventional and 67% balanced accuracy.
The researchers concluded, “Brain FDG-PET scans read with Neurostat 3D-SSP displays accurately detect Alzheimer's disease in patients presenting with primary progressive aphasia or corticobasal syndrome as focal onset dementias. In these diagnostically challenging cohorts, FDG-PET imaging can provide more accurate diagnoses enabling more appropriate therapy.”
Key Chest CT Parameters for Body Composition May be Prognostic for Patients with Resectable NSCLC
February 11th 2025A high intermuscular adipose index has a 49 percent increased likelihood of being associated with lower overall survival in patients with resectable non-small cell lung cancer (NSCLC), according to new research.
Comparative AI Study Shows Merits of RapidAI LVO Software in Stroke Detection
February 6th 2025The Rapid LVO AI software detected 33 percent more cases of large vessel occlusion (LVO) on computed tomography angiography (CTA) than Viz LVO AI software, according to a new comparative study presented at the International Stroke Conference (ISC).
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Computed Tomography Study Assesses Model for Predicting Recurrence of Non-Small Cell Lung Cancer
January 31st 2025A predictive model for non-small cell lung cancer (NSCLC) recurrence, based on clinical parameters and CT findings, demonstrated an 85.2 percent AUC and 83.3 percent sensitivity rate, according to external validation testing in a new study.