Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression

Antonio Martinez-Torteya, Juan Rodriguez-Rojas, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Victor Trevinõ, Jose Tamez-Penã

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e-11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

Original languageEnglish
Article number031005
Pages (from-to)031005
JournalJournal of Medical Imaging
Volume1
Issue number3
DOIs
Publication statusPublished - 1 Oct 2014
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE).

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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