Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers.

PURPOSE: To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. MATERIALS AND METHODS: All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). RESULTS: Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08). CONCLUSION: Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.

A pattern-based approach to bowel obstruction in the newborn.

Intestinal obstruction is common in newborns, and the radiologist plays a critical role in the care of these children. Diagnosing and managing the potentially obstructed newborn can be challenging, especially given the myriad underlying pathologies that range from benign to acutely life-threatening. A familiarity with the most common diagnoses is essential, but equally important to the radiologist is a systematic approach to management of the child in this setting. We propose an approach based on the recognition of eight radiographic patterns, five upper gastrointestinal examination (UGI) patterns and four contrast enema patterns. Recognition of these patterns directs further imaging when necessary and allows triage of children who can be managed medically, those requiring elective or urgent surgery and those requiring emergent surgery.