Correlation of [18F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach

Li, Jing; Antonecchia, Emanuele; Camerlenghi, Marco; Chiaravalloti, Agostino; Chu, Qian; Costanzo, Alfonso Di; Li, Zhen; Wan, Lin; Zhang, Xiangsong; D’Ascenzo, N.; Schillaci, Orazio; Xie, Qingguo

doi:10.1186/s13550-021-00774-x

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…In our study, as in several previous ones, 17,18 β-amyloid deposition did not significantly differ between EOAD and LOAD, indicating no close association between Aβ deposition and onset age. Conversely, others have reported that Aβ deposition is more pronounced in EOAD 11,18,36,37 . The discrepancy might be related to differences in group categorization, sample size, and methodology between studies.…”

Section: Discussionmentioning

confidence: 87%

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Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI

Xu¹,

Ruan²,

Liu³

et al. 2023

Clin Nucl Med

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PurposeEarly-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework–based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD.MethodsPatients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18F-FDG and 18F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated.ResultsOne hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex (P = 0.515) and education (P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD (P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13).ConclusionsMultiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.

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Section: Discussionmentioning

confidence: 87%

“…Conversely, others have reported that Aβ deposition is more pronounced in EOAD. 11,18,36,37 The discrepancy might be related to differences in group categorization, sample size, and methodology between studies.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI

Xu¹,

Ruan²,

Liu³

et al. 2023

Clin Nucl Med

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“…It also reflects the degree of association between different voxels in the whole brain [41]. Previous study suggested that early onset of AD showed more complex pattern in T1W images [42]. GLRLM relates to the coarseness and complexity, which reflect the degree of association between different pixels in the whole brain [41].…”

Section: Radiomics Features In Ad MCI and Cnmentioning

confidence: 99%

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

Chau

Tang

2022

Life

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This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

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A Comprehensive Review and Current Methods for Classifying Alzheimer's Disease Using Feature Extraction and Machine Learning Techniques

Chithra¹,

Vijayabhanu²

2023

Proceedings of Third International Conference on Sustainable Expert Systems

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Correlation of [18F]florbetaben textural features and age of onset of Alzheimer’s disease: a principal components analysis approach

Cited by 3 publications

References 53 publications

Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI

Characterizing Early-Onset Alzheimer Disease Using Multiprobe PET/MRI

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

A Comprehensive Review and Current Methods for Classifying Alzheimer's Disease Using Feature Extraction and Machine Learning Techniques

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