Based on Tau PET Radiomics Analysis for the Classification of Alzheimer's Disease and Mild Cognitive Impairment

Fangyang Jiao; Min Wang; Xiaoming Sun; Zizhao Ju; Jiaying Lu; Luyao Wang; Jiehui Jiang; Chuantao Zuo

doi:10.3390/brainsci13020367

Based on Tau PET Radiomics Analysis for the Classification of Alzheimer's Disease and Mild Cognitive Impairment

Brain Sci. 2023 Feb 20;13(2):367. doi: 10.3390/brainsci13020367.

Authors

Fangyang Jiao¹, Min Wang², Xiaoming Sun³, Zizhao Ju¹, Jiaying Lu¹, Luyao Wang², Jiehui Jiang², Chuantao Zuo¹

Affiliations

¹ Department of Nuclear Medicine and PET Center, National Center for Neurological Diseases and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200235, China.
² Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai 200444, China.
³ School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.

Abstract

Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) are closely associated with Tau proteins accumulation. In this study, we aimed to implement radiomics analysis to discover high-order features from pathological biomarker and improve the classification accuracy based on Tau PET images. Two cross-racial independent cohorts from the ADNI database (121 AD patients, 197 MCI patients and 211 normal control (NC) subjects) and Huashan hospital (44 AD patients, 33 MCI patients and 36 NC subjects) were enrolled. The radiomics features of Tau PET imaging of AD related brain regions were computed for classification using a support vector machine (SVM) model. The radiomics model was trained and validated in the ADNI cohort and tested in the Huashan hospital cohort. The standard uptake value ratio (SUVR) and clinical scores model were also performed to compared with radiomics analysis. Additionally, we explored the possibility of using Tau PET radiomics features as a good biomarker to make binary identification of Tau-negative MCI versus Tau-positive MCI or apolipoprotein E (ApoE) ε4 carrier versus ApoE ε4 non-carrier. We found that the radiomics model demonstrated best classification performance in differentiating AD/MCI patients and NC in comparison to SUVR and clinical scores models, with an accuracy of 84.8 ± 4.5%, 73.1 ± 3.6% in the ANDI cohort. Moreover, the radiomics model also demonstrated greater performance in diagnosing AD than other methods in the Huashan hospital cohort, with an accuracy of 81.9 ± 6.1%. In addition, the radiomics model also showed the satisfactory classification performance in the MCI-tau subgroup experiment (72.3 ± 3.5%, 71.9 ± 3.6% and 63.7 ± 5.9%) and in the MCI-ApoE subgroup experiment (73.5 ± 4.3%, 70.1 ± 3.9% and 62.5 ± 5.4%). In conclusion, our study showed that based on Tau PET radiomics analysis has the potential to guide and facilitate clinical diagnosis, further providing evidence for identifying the risk factors in MCI patients.

Keywords: Alzheimer’s Disease; Mild Cognitive Impairment; Tau PET; radiomics.

Abstract

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