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

Antonio Martínez-Torteya, Juan Rodríguez-Rojas, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Victor Treviño, José G. Tamez-Peña

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

An early diagnosis of Alzheimera'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 Alzheimera'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. © 2014 SPIE.
Original languageEnglish
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventProgress in Biomedical Optics and Imaging - Proceedings of SPIE -
Duration: 1 Jan 2014 → …

Conference

ConferenceProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Period1/1/14 → …

Fingerprint

progressions
Magnetic resonance imaging
Disease Progression
Alzheimer Disease
Biomarkers
textures
Textures
biomarkers
predictions
Logistic Models
Neuroimaging
Early Diagnosis
Risk analysis
Databases
logistics
Logistics
Feature extraction
regression analysis

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Martínez-Torteya, A., Rodríguez-Rojas, J., Celaya-Padilla, J. M., Galván-Tejada, J. I., Treviño, V., & Tamez-Peña, J. G. (2014). MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression. Paper presented at Progress in Biomedical Optics and Imaging - Proceedings of SPIE, . https://doi.org/10.1117/12.2043903
Martínez-Torteya, Antonio ; Rodríguez-Rojas, Juan ; Celaya-Padilla, José M. ; Galván-Tejada, Jorge I. ; Treviño, Victor ; Tamez-Peña, José G. / MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression. Paper presented at Progress in Biomedical Optics and Imaging - Proceedings of SPIE, .
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abstract = "An early diagnosis of Alzheimera'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 Alzheimera'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. {\circledC} 2014 SPIE.",
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Martínez-Torteya, A, Rodríguez-Rojas, J, Celaya-Padilla, JM, Galván-Tejada, JI, Treviño, V & Tamez-Peña, JG 2014, 'MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression' Paper presented at Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 1/1/14, . https://doi.org/10.1117/12.2043903

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression. / Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014. Paper presented at Progress in Biomedical Optics and Imaging - Proceedings of SPIE, .

Research output: Contribution to conferencePaper

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AU - Martínez-Torteya, Antonio

AU - Rodríguez-Rojas, Juan

AU - Celaya-Padilla, José M.

AU - Galván-Tejada, Jorge I.

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AU - Tamez-Peña, José G.

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Martínez-Torteya A, Rodríguez-Rojas J, Celaya-Padilla JM, Galván-Tejada JI, Treviño V, Tamez-Peña JG. MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression. 2014. Paper presented at Progress in Biomedical Optics and Imaging - Proceedings of SPIE, . https://doi.org/10.1117/12.2043903