CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.

Head-to-head comparison of amplified plasmonic exosome Aß42 platform and single-molecule array immunoassay in a memory clinic cohort.

BACKGROUND AND PURPOSE: Various blood biomarkers reflecting brain amyloid-ß (Aß) load have recently been proposed with promising results. However, to date, no comparative study amongst blood biomarkers has been reported. Our objective was to examine the diagnostic performance and cost effectiveness of three blood biomarkers on the same cohort. METHODS: Using the same cohort (n = 68), the performances of the single-molecule array (Simoa) Aß40, Aß42, Aß42/Aß40 and the amplified plasmonic exosome (APEX) Aß42 blood biomarkers were compared using amyloid positron emission tomography (PET) as the reference standard. The extent to which these blood tests can reduce the recruitment cost of clinical trials was also determined by identifying amyloid positive (Aß+) participants. RESULTS: Compared to Simoa biomarkers, APEX-Aß42 showed significantly higher correlations with amyloid PET retention values and excellent diagnostic performance (sensitivity 100%, specificity 93.3%, area under the curve 0.995). When utilized for clinical trial recruitment, our simulation showed that pre-screening with blood biomarkers followed by a confirmatory amyloid PET imaging would roughly half the cost (56.8% reduction for APEX-Aß42 and 48.6% for Simoa-Aß42/Aß40) compared to the situation where only PET imaging is used. Moreover, with 100% sensitivity, APEX-Aß42 pre-screening does not increase the required number of initial participants. CONCLUSIONS: With its high diagnostic performance, APEX is an ideal candidate for Aß+ subject identification, monitoring and primary care screening, and could efficiently enrich clinical trials with Aß+ participants whilst halving recruitment costs.