Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model.Meth...
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2018.00618/full |
| _version_ | 1818282239105957888 |
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| author | Qi Feng Qi Feng Yuanjun Chen Zhengluan Liao Hongyang Jiang Dewang Mao Mei Wang Enyan Yu Zhongxiang Ding |
| author_facet | Qi Feng Qi Feng Yuanjun Chen Zhengluan Liao Hongyang Jiang Dewang Mao Mei Wang Enyan Yu Zhongxiang Ding |
| author_sort | Qi Feng |
| collection | DOAJ |
| description | Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model.Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects.Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively.Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD. |
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| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-13T00:17:51Z |
| publishDate | 2018-07-01 |
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| series | Frontiers in Neurology |
| spelling | doaj.art-838dac63c34744c79d0a26fc96f705e62022-12-22T00:05:44ZengFrontiers Media S.A.Frontiers in Neurology1664-22952018-07-01910.3389/fneur.2018.00618395109Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control StudyQi Feng0Qi Feng1Yuanjun Chen2Zhengluan Liao3Hongyang Jiang4Dewang Mao5Mei Wang6Enyan Yu7Zhongxiang Ding8Bengbu Medical College, Bengbu, ChinaDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaGE Healthcare Life Sciences, Guangzhou, ChinaDepartment of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Psychiatry, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaBackground: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model.Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects.Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively.Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD.https://www.frontiersin.org/article/10.3389/fneur.2018.00618/fullmagnetic resonance imagingAlzheimer's diseasecorpus callosumradiomicsneuroimaging |
| spellingShingle | Qi Feng Qi Feng Yuanjun Chen Zhengluan Liao Hongyang Jiang Dewang Mao Mei Wang Enyan Yu Zhongxiang Ding Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study Frontiers in Neurology magnetic resonance imaging Alzheimer's disease corpus callosum radiomics neuroimaging |
| title | Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study |
| title_full | Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study |
| title_fullStr | Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study |
| title_full_unstemmed | Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study |
| title_short | Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study |
| title_sort | corpus callosum radiomics based classification model in alzheimer s disease a case control study |
| topic | magnetic resonance imaging Alzheimer's disease corpus callosum radiomics neuroimaging |
| url | https://www.frontiersin.org/article/10.3389/fneur.2018.00618/full |
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