A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI
Objective: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET)...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/12/8/1067 |
| _version_ | 1797411002753482752 |
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| author | Yan Zhao Jieming Zhang Yue Chen Jiehui Jiang |
| author_facet | Yan Zhao Jieming Zhang Yue Chen Jiehui Jiang |
| author_sort | Yan Zhao |
| collection | DOAJ |
| description | Objective: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. Methods: In this study, we selected tau-PET scans from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. Results: Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. Conclusions: Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC. |
| first_indexed | 2024-03-09T04:38:41Z |
| format | Article |
| id | doaj.art-0f6de586c4b04b7885aff6dfe2fc7f02 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T04:38:41Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-0f6de586c4b04b7885aff6dfe2fc7f022023-12-03T13:23:44ZengMDPI AGBrain Sciences2076-34252022-08-01128106710.3390/brainsci12081067A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNIYan Zhao0Jieming Zhang1Yue Chen2Jiehui Jiang3Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaNuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, ChinaNuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou 646000, ChinaObjective: We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans. Methods: In this study, we selected tau-PET scans from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments. Results: Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD. Conclusions: Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.https://www.mdpi.com/2076-3425/12/8/1067Alzheimer’s diseasemild cognitive impairmenttau positron emission tomographydeep learning radiomics |
| spellingShingle | Yan Zhao Jieming Zhang Yue Chen Jiehui Jiang A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI Brain Sciences Alzheimer’s disease mild cognitive impairment tau positron emission tomography deep learning radiomics |
| title | A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI |
| title_full | A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI |
| title_fullStr | A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI |
| title_full_unstemmed | A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI |
| title_short | A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI |
| title_sort | novel deep learning radiomics model to discriminate ad mci and nc an exploratory study based on tau pet scans from adni |
| topic | Alzheimer’s disease mild cognitive impairment tau positron emission tomography deep learning radiomics |
| url | https://www.mdpi.com/2076-3425/12/8/1067 |
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