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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

 Yae Won Park  ;  Dongmin Choi  ;  Mina Park  ;  Sung Jun Ahn  ;  Sung Soo Ahn  ;  Sang Hyun Suh  ;  Seung-Koo Lee 
 JOURNAL OF ALZHEIMERS DISEASE, Vol.79(2) : 483-491, 2021-01 
Journal Title
Issue Date
Amyloid ; artificial intelligence ; machine learning ; mild cognitive impairment ; radiomics
Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Park, Mina(박미나) ORCID logo https://orcid.org/0000-0002-2005-7560
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Suh, Sang Hyun(서상현) ORCID logo https://orcid.org/0000-0002-7098-4901
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Ahn, Sung Jun(안성준) ORCID logo https://orcid.org/0000-0003-0075-2432
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
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