Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Breast, colorectal, and prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition–Norfolk in relation to phytoestrogen intake derived from an improved database

Background: The characterization of phytoestrogen intake and cancer risk has been hindered by the absence of accurate dietary phytoestrogen values. Objective: We examined the risk of breast, colorectal, and prostate cancers relative to phytoestrogen intake on the basis of a comprehensive database. Design: Demographic and anthropometric characteristics, a medical history, and 7-d records of diet were collected prospectively from participants (aged 40–79 y) in the European Prospective Investigation into Cancer and Nutrition–Norfolk (EPIC-Norfolk). Five hundred nine food items were analyzed by liquid chromatography–mass spectrometry/mass spectrometry, and 13C3-labeled internal standards were analyzed for isoflavones (genistein, daidzein, glycitein, biochanin A, and formononetin), lignans (secoisolariciresinol and matairesinol), and enterolignans from gut microbial metabolism in animal food sources (equol and enterolactone). From the direct analysis, values for 10,708 foods were calculated. Odds ratios (ORs) for breast (244 cases, 941 controls), colorectal (221 cases, 886 controls), and prostate (204 cases, 812 controls) cancers were calculated relative to phytoestrogen intake. Results: Phytoestrogen intake was not associated with breast cancer among women or colorectal cancer among men. Among women, colorectal cancer risk was inversely associated with enterolactone (OR: 0.33; 95% CI: 0.14, 0.74) and total enterolignans (OR: 0.32; 95% CI: 0.13, 0.79), with a positive trend detected for secoisolariciresinol (OR: 1.60; 95% CI: 0.96, 2.69). A positive trend between enterolignan intake and prostate cancer risk (OR: 1.27; 95% CI: 0.97, 1.66) was attenuated after adjustment for dairy intake (OR: 1.19; 95% CI: 0.77, 1.82). Conclusion: Dietary phytoestrogens may contribute to the risk of colorectal cancer among women and prostate cancer among men.