Jiehui Jiang1, Min Wang2, Ian Alberts3, Xiaoming Sun2, Taoran Li4, Axel Rominger3, Chuantao Zuo5,6, Ying Han7,8,9,10, Kuangyu Shi3,11, For The Alzheimer's Disease Neuroimaging Initiative. 1. Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China. jiangjiehui@shu.edu.cn. 2. Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China. 3. Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland. 4. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China. 5. PET Center, Huashan Hospital, Fudan University, Shanghai, China. zuochuantao@fudan.edu.cn. 6. Human Phenome Institute, Fudan University, Shanghai, China. zuochuantao@fudan.edu.cn. 7. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China. hanying@xwh.ccmu.edu.cn. 8. Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. hanying@xwh.ccmu.edu.cn. 9. School of Biomedical Engineering, Hainan University, Haikou, China. hanying@xwh.ccmu.edu.cn. 10. National Clinical Research Center for Geriatric Disorders, Beijing, China. hanying@xwh.ccmu.edu.cn. 11. Department of Informatics, Technische Universität München, Munich, Germany.
Abstract
BACKGROUND: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. METHOD: FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. RESULTS: The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. CONCLUSION: The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.
BACKGROUND: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. METHOD: FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. RESULTS: The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. CONCLUSION: The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.
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