CT-derived brain volumes and plasma p-Tau217 for risk stratification of amyloid positivity in early-stage Alzheimer's disease

Abstract

BackgroundEarly detection of amyloid-beta (A beta) pathology is critical for timely intervention in Alzheimer's disease (AD). While A beta positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers are accurate, their high cost and limited accessibility hinder routine use. We developed a computed tomography (CT)-based, two-stage workflow combining CT-derived atrophy patterns with plasma phosphorylated tau 217 (p-Tau217) to predict A beta PET positivity.MethodsIn this cohort of 616 participants (521 with mild cognitive impairment (MCI], 95 with early dementia of Alzheimer's type (DAT]; age 60-93 years), CT, p-Tau217 assays, and A beta PET were performed. A random forest model incorporating CT-derived regional W-scores and apolipoprotein E epsilon 4 (APOE epsilon 4) status stratified individuals into low-, intermediate-, and high-risk groups. p-Tau217 testing was reserved for the intermediate-risk group.ResultsAt a 95% sensitivity/specificity threshold, CT-based stratification yielded a low-risk negative predictive value (NPV) of 95.8% (93.0-98.6%) and a high-risk positive predictive value (PPV) of 98.4% (96.8-100.0%), with 28.2% classified as intermediate-risk. Targeted plasma testing of intermediate-risk group improved the overall PPV to 92.8% (88.5-97.1%) and the overall NPV to 88.9% (78.6-99.2%), achieving an overall accuracy of 95.8% (94.2-97.4%). The CT-based workflow's accuracy was non-inferior to our MRI-based method (area under the curve 0.96 vs. 0.95; p = 0.14).ConclusionsThis CT-based, two-stage approach is a cost-effective, scalable alternative to MRI-based strategies, leveraging routine CT and selective p-Tau217 testing to enhance early AD detection and optimize resource utilization in clinical practice.