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Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.
Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC).
Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models.
Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.
Keywords
amyloid, artificial intelligence, machine learning, mild cognitive impairment, radiomics
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
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History
CURRENT STATUS: Posted
View the published article at Journal of Alzheimer's Disease.
Version 1
Posted 10 Mar, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cognitive Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Alzheimer's Disease.
Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.
Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC).
Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models.
Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
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