Cross-sectional Magnetic Resonance Imaging Data Research Articles

A comprehensive analysis of APOE genotype effects on human brain structure in the UK Biobank

Alzheimer’s disease (AD) risk is increased in carriers of the apolipoprotein E (APOE) ε4 allele and decreased in ε2 allele carriers compared with the ε3ε3 genotype. The aim of this study was to determine whether: the APOE genotype affects brain grey (GM) or white matter (WM) structure; and if differences exist, the age when they become apparent and whether there are differential effects by sex. We used cross-sectional magnetic resonance imaging data from ~43,000 (28,494 after pre-processing) white British cognitively healthy participants (7,446 APOE ε4 carriers) aged 45–80 years from the UK Biobank cohort and investigated image-derived phenotypes (IDPs). We observed no statistically significant effects of APOE genotype on GM structure volumes or median T2* in subcortical structures, a measure related to iron content. The volume of white matter hyperintensities differed significantly between APOE genotype groups with higher volumes in APOE ε4ε4 (effect size 0.14 standard deviations [SD]) and ε3ε4 carriers (effect size 0.04 SD) but no differences in ε2 carriers compared with ε3ε3 carriers. WM integrity measures in the dorsal (mean diffusivity [MD]) and ventral cingulum (MD and intracellular volume fraction), posterior thalamic radiation (MD and isotropic volume fraction) and sagittal stratum (MD) indicated lower integrity in APOE ε4ε4 carriers (effect sizes around 0.2–0.3 SD) and ε3ε4 (effect sizes around 0.05 SD) carriers but no differences in ε2 carriers compared with the APOE ε3ε3 genotype. Effects did not differ between men and women. APOE ε4 homozygotes had lower WM integrity specifically at older ages with a steeper decline of WM integrity from the age of 60 that corresponds to around 5 years greater “brain age”. APOE genotype affects various white matters measures, which might be indicative of preclinical AD processes. This hypothesis can be assessed in future when clinical outcomes become available.

Prediction of whole body composition utilizing cross-sectional abdominal imaging in pediatrics.

Although body composition is an important determinant of pediatric health outcomes, we lack tools to routinely assess it in clinical practice. We define models to predict whole-body skeletal muscle and fat composition, as measured by dual X-ray absorptiometry (DXA) or whole-body magnetic resonance imaging (MRI), in pediatric oncology and healthy pediatric cohorts, respectively. Pediatric oncology patients (≥5 to ≤18 years) undergoing an abdominal CT were prospectively recruited for a concurrent study DXA scan. Cross-sectional areas of skeletal muscle and total adipose tissue at each lumbar vertebral level (L1-L5) were quantified and optimal linear regression models were defined. Whole body and cross-sectional MRI data from a previously recruited cohort of healthy children (≥5 to ≤18 years) was analyzed separately. Eighty pediatric oncology patients (57% male; age range 5.1-18.4 y) were included. Cross-sectional areas of skeletal muscle and total adipose tissue at lumbar vertebral levels (L1-L5) were correlated with whole-body lean soft tissue mass (LSTM) (R2 = 0.896-0.940) and fat mass (FM) (R2 = 0.874-0.936) (p < 0.001). Linear regression models were improved by the addition of height for prediction of LSTM (adjusted R2 = 0.946-0.971; p < 0.001) and by the addition of height and sex (adjusted R2 = 0.930-0.953) (p < 0.001)) for prediction of whole body FM. High correlation between lumbar cross-sectional tissue areas and whole-body volumes of skeletal muscle and fat, as measured by whole-body MRI, was confirmed in an independent cohort of 73 healthy children. Regression models can predict whole-body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.

A systematic analysis of assorted machine learning classifiers to assess their potential in accurate prediction of dementia

PurposeThis study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.Design/methodology/approachDementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p &lt; 0.01. The authors employed 20 ML classifiers.FindingsThe mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.Originality/valueThe results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50.

Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Virtual pancreatoscopy of mucin-producing pancreatic tumors.

We used computer-based virtual endoscopy techniques as a novel approach to clarify the three-dimensional (3D) surgical anatomy of the pancreas and of mucin-producing pancreatic tumors. Thirteen cases (18 lesions) of mucin-producing pancreatic tumors were investigated by virtual pancreatoscopy. Virtual endoscopic images were generated with virtual endoscopy software application on UNIX workstations. We created surface-rendered virtual endoscopic images derived from a computer reconstruction of the cross-sectional magnetic resonance imaging data. Virtual endoscopy could visualize the surfaces of the pancreatic duct and the bile duct, and also demonstrated all cystic tumors. The surfaces of malignant mucin-producing pancreatic tumors were illustrated as being more irregular than those of benign lesions. The virtual endoscopic technique could demonstrate not only a surface-rendered endoscopic image of the tumors but also a 3D reconstructed image of the pancreas. The relationship to anatomic structures located outside the surfaces is continuously maintained and displayed at the same time. Virtual pancreatoscopy was useful for surgical planning of minimally invasive resection of the pancreas.

Virtual Pancreatoscopy of Mucin-Producing Pancreatic Tumors

We used computer-based virtual endoscopy techniques as a novel approach to clarify the three-dimensional (3D) surgical anatomy of the pancreas and of mucin-producing pancreatic tumors. Thirteen cases (18 lesions) of mucin-producing pancreatic tumors were investigated by virtual pancreatoscopy. Virtual endoscopic images were generated with virtual endoscopy software application on UNIX workstations. We created surface-rendered virtual endoscopic images derived from a computer reconstruction of the cross-sectional magnetic resonance imaging data. Virtual endoscopy could visualize the surfaces of the pancreatic duct and the bile duct, and also demonstrated all cystic tumors. The surfaces of malignant mucin-producing pancreatic tumors were illustrated as being more irregular than those of benign lesions. The virtual endoscopic technique could demonstrate not on ly a surface-rendered endoscopic image of the tumors but also a 3D reconstructed image of the pancreas. The relationship to anatomic structures located outside the surfaces is continuously maintained and displayed at the same time. Virtual pancreatoscopy was useful for surgical planning of minimally invasive resection of the pancreas.