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Proceedings Paper

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression
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Paper Abstract

An early diagnosis of Alzheimer’s disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer’s Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

Paper Details

Date Published: 20 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903526 (20 March 2014); doi: 10.1117/12.2043903
Show Author Affiliations
Antonio Martínez -Torteya, Tecnológico de Monterrey (Mexico)
Juan Rodríguez-Rojas, Tecnológico de Monterrey (Mexico)
José M. Celaya-Padilla, Tecnológico de Monterrey (Mexico)
Jorge I. Galván-Tejada, Tecnológico de Monterrey (Mexico)
Victor Treviño, Tecnológico de Monterrey (Mexico)
José G. Tamez-Peña, Tecnológico de Monterrey (Mexico)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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