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Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment
BACKGROUND: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using (18)F-fluorodeoxy-glucose positron e...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444412/ https://www.ncbi.nlm.nih.gov/pubmed/30956687 http://dx.doi.org/10.1177/1756286419838682 |
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author | Li, Yupeng Jiang, Jiehui Lu, Jiaying Jiang, Juanjuan Zhang, Huiwei Zuo, Chuantao |
author_facet | Li, Yupeng Jiang, Jiehui Lu, Jiaying Jiang, Juanjuan Zhang, Huiwei Zuo, Chuantao |
author_sort | Li, Yupeng |
collection | PubMed |
description | BACKGROUND: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using (18)F-fluorodeoxy-glucose positron emission tomography ((18)F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. METHODS: In this study, (18)F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. RESULTS: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. CONCLUSION: The research in this paper proved that the novel approach based on high-order radiomic features extracted from (18)F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis. |
format | Online Article Text |
id | pubmed-6444412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-64444122019-04-05 Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment Li, Yupeng Jiang, Jiehui Lu, Jiaying Jiang, Juanjuan Zhang, Huiwei Zuo, Chuantao Ther Adv Neurol Disord Original Research BACKGROUND: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using (18)F-fluorodeoxy-glucose positron emission tomography ((18)F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. METHODS: In this study, (18)F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. RESULTS: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. CONCLUSION: The research in this paper proved that the novel approach based on high-order radiomic features extracted from (18)F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis. SAGE Publications 2019-03-29 /pmc/articles/PMC6444412/ /pubmed/30956687 http://dx.doi.org/10.1177/1756286419838682 Text en © The Author(s), 2019 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Li, Yupeng Jiang, Jiehui Lu, Jiaying Jiang, Juanjuan Zhang, Huiwei Zuo, Chuantao Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title_full | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title_fullStr | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title_full_unstemmed | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title_short | Radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment |
title_sort | radiomics: a novel feature extraction method for brain neuron degeneration disease using (18)f-fdg pet imaging and its implementation for alzheimer’s disease and mild cognitive impairment |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444412/ https://www.ncbi.nlm.nih.gov/pubmed/30956687 http://dx.doi.org/10.1177/1756286419838682 |
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