Texture and signal features from hippocampal T2 maps as biomarkers for MCI to AD progression

Alejandro I. Trejo-Castro, Ricardo A. Caballero-Luna, Jose A. Garnica-Lopez, Fernando Vega-Lara, Jose M. Celaya-Padilla, Jose G. Tamez-Pena, Antonio Martinez-Torteya

Resultado de la investigación

1 Cita (Scopus)

Resumen

Alzheimer's disease (AD) is the most common type of dementia and predicting who will convert from Mild Cognitive Impairment (MCI) to AD is crucial to patient benefits as well as medical research. To fulfill this purpose, in recent years it has been reported that the texture of magnetic resonance images can be an effective biomarker. In this study we used images from the Alzheimer's Disease Neuroimaging Initiative database to create T2 maps and identify features related to the texture and signal distribution for the prediction of AD. We extracted 3S features from the left and right hippocampus for 40 patients with MCI who either progressed to AD (18) or remained stable (22) and measured the mean and absolute difference of these contralateral features. We also kept the original volume of each region, yielding a total of 7S features. We used 7 machine learning methods to analyze whether by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify who would develop the disease. We found 11 features significantly different between groups. Furthermore, all but one of the machine learning methods improved their accuracy by adding the signal- and texture-related features, and the volumetric information was non-significant. Our results suggest that these imaging features from hippocampal T2 maps should be further investigated as potential MRI biomarkers for the prediction of AD
Idioma originalEnglish
Título de la publicación alojadaProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditoresTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas772-777
Número de páginas6
ISBN (versión digital)9781728162157
ISBN (versión impresa)9781728162157
DOI
EstadoPublished - 16 dic. 2020
Evento2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul
Duración: 16 dic. 202019 dic. 2020

Serie de la publicación

NombreProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
País/TerritorioKorea, Republic of
CiudadVirtual, Seoul
Período16/12/2019/12/20

Nota bibliográfica

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Informática aplicada
  • Gestión y sistemas de información
  • Medicina (miscelánea)
  • Informática aplicada a la salud

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