ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer's Disease by Means of Textures Analysis on Magnetic Resonance Images

The current criteria for diagnosing Alzheimer's disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become on...

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Bibliographic Details
Published in:	Diagnostics (Basel) Vol. 8; no. 3; p. 47
Main Authors:	López-Gómez, Carlos, Ortiz-Ramón, Rafael, Mollá-Olmos, Enrique, Moratal, David
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 19-07-2018 MDPI
Subjects:	Alzheimer’s disease Atrophy Biomarkers Cognitive ability Dementia Disease magnetic resonance imaging Medical imaging mild cognitive impairment Neurodegeneration NMR Nuclear magnetic resonance Software Statistical methods Studies texture analysis Spain magnetic resonance imaging mild cognitive impairment Alzheimer’s disease software biomarkers texture analysis
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Summary:	The current criteria for diagnosing Alzheimer's disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant ( < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. These authors contributed equally to this work.
ISSN:	2075-4418 2075-4418
DOI:	10.3390/diagnostics8030047