TY - JOUR
T1 - 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
AU - Martinez-Torteya, Antonio
AU - Rodriguez-Rojas, Juan
AU - Celaya-Padilla, José M.
AU - Galván-Tejada, Jorge I.
AU - Trevinõ, Victor
AU - Tamez-Penã, Jose
N1 - Publisher Copyright:
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE).
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
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UR - https://www.mendeley.com/catalogue/e3d71a94-d4ef-3520-ad57-095249b4aa99/
U2 - 10.1117/1.JMI.1.3.031005
DO - 10.1117/1.JMI.1.3.031005
M3 - Article
C2 - 26158047
SN - 2329-4302
VL - 1
SP - 031005
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 3
M1 - 031005
ER -