Combined Quantitative amyloid-β PET and Structural MRI Features Improve Alzheimer's Disease Classification in Random Forest Model - A Multicenter Study.

Autor:	Bao YW; Department of Medical Imaging Center, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China (Y-W.B.)., Wang ZJ; Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.)., Shea YF; Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)., Chiu PK; Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)., Kwan JS; Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)., Chan FH; Department of Medicine, Queen Mary Hospital, Hong Kong SAR, China (Y-F.S., P.K-C.C., J.S.K., F.H-W.C.)., Mak HK; Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China (Z-J.W., H.K-F.M.). Electronic address: makkf@hku.hk.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Dec; Vol. 31 (12), pp. 5154-5163. Date of Electronic Publication: 2024 Jul 12.
DOI:	10.1016/j.acra.2024.06.040
Abstrakt:	Rationale and Objectives: Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-β and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aβ PET features and whether combining Aβ PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD. Material and Methods: We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aβ features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification. Results: The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aβ PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aβ PET features contributed more to differentiating AD from others. Conclusion: Our study demonstrated the discriminative ability of Aβ PET features in differentiating AD from OHC and MCI. A combination of Aβ PET and structural MRI features can improve the RF model performance. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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