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Quantifying heterogeneity in Alzheimer’s disease (AD) risk is critical for individualized care and management. Recent attempts to assess AD heterogeneity have used structural (magnetic resonance imaging (MRI)-based) or functional (Aβ or tau) imaging, which focused on generating quartets of atrophy patterns and protein spreading, respectively. Here we present a computational framework that facilitated the identification of subtypes based on their risk of progression to AD. We used cerebrospinal fluid (CSF) measures of Aβ from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n=544, discovery cohort) as well as the National Alzheimer’s Coordinating Center (NACC) (n=508, validation cohort), and risk-stratified individuals with mild cognitive impairment (MCI) into quartiles (high-risk (H), intermediate-high risk (IH), intermediate-low risk (IL), and low-risk (L)). Patients were divided into subgroups utilizing patterns of brain atrophy found in each of these risk-stratified quartiles. We found H subjects to have a greater risk of AD progression compared to the other subtypes at 2- and 4-years in both the discovery and validation cohorts (ADNI: H subtype versus all others, p < 0.05 at 2 and 4 years; NACC: H vs. IL and LR at 2 years, p < 0.05, and a trend toward higher risk vs. IH, and p < 0.05 vs. IH, and L risk groups at 48 months with a trend toward lower survival vs. IL). Using MRI-based neural models that fused various deep neural networks with survival analysis, we then predicted MCI to AD conversion. We used these models to identify subtype-specific regions that demonstrate the largest levels of atrophy-related importance, which had minimal overlap (Average pairwise Jaccard Similarity in regions between the top 5 subtypes, 0.25±0.05 (± std)). Neuropathologic changes characteristic of AD were present across all subtypes in comparable proportions (Chi-square test, p>0.05 for differences in ADNC, n=31). Our risk-based approach to subtyping individuals provides an objective means to intervene and tailor care management strategies at early stages of cognitive decline.

Purpose To extend the investigation of price transparency and variability to medical imaging. Methods Eighteen upper-tier academic hospitals identified by U.S. News & World Report and 14 of the 100 largest private radiology practices in the country identified by the Radiology Business Journal were contacted by telephone between December 2013 and February 2014 to determine the cash price for a noncontrast head CT. The price for a noncontrast head CT was chosen to assess price transparency in medical imaging because it represents a standard imaging examination with minimal differences in quality. Results Fourteen upper-tier academic hospitals (78%) and 11 private practices (79%) were able to provide prices for a noncontrast head CT. There was no significant difference between the proportions of upper-tier academic hospitals and private practices that were able to provide prices for a noncontrast head CT ( P = .96). The average total price for the upper-tier academic hospitals was $1,390.12 ± $686.13, with the price ranging from $391.62 to $2,015. The average total price for the private practices was $681.60 ± $563.58, with the total price ranging from $211 to $2,200. Conclusions Prices for a noncontrast head CT study were readily available from the vast majority of upper-tier academic hospitals and private practices, although there was tremendous variation in the price estimates both within and between the upper-tier academic hospitals and private practices. Routine medical imaging thus appears to be more price transparent compared with other health care services.