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Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine

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Abstract

Purpose

Alzheimer’s disease (AD) is the most common type of dementia, and its early diagnosis has become a crucial issue. Machine learning provides a systematic and objective approach in classification. Currently, there are many studies using several kinds of neuroimaging modalities to perform classification in dementia. Support vector machine (SVM) is one of machine learning based classification algorithm which is able to retain favorable classification accuracy even with small sample sizes. Our aim is to investigate the feasibility of using dual PET biomarkers in combination with SVM for AD diagnosis in small sample sizes.

Methods

This study collected PET (18F-FDG and 11C-PiB) and T1 MRI image of 79 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, including 20 AD, 27 mild cognitive impairment (MCI) subjects, and 32 normal controls (NCs), and performed classification using the SVM algorithm with the quantification of the two PET biomarkers, and finally compared the classification results of each brain region.

Results

In the classification between diseased (AD and MCI) and NC group, we found that the accuracy, sensitivity and specificity mean in temporal cortex are the highest.

Conclusions

Overall, using dual PET biomarkers in combination with SVM shows a certain feasibility and clinical value in the diagnosis of AD, especially in the temporal cortex.

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Acknowledgements

The authors are grateful to the grant support from Ministry of Science and Technology, Taiwan, R.O.C. under Grant No. MOST 108-2314-B-075-007. Data used in preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (https://www.adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

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Authors and Affiliations

  1. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, 112, Taiwan

    Bang-Hung Yang, Jyh-Cheng Chen, Wen-Hsiang Chou, Ren‑Shyan Liu & Cheng-Han Wu

  2. Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, 112, Taiwan

    Bang-Hung Yang, Wen-Sheng Huang, Ren‑Shyan Liu & Cheng-Han Wu

  3. Department of Nuclear Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114, Taiwan

    Wen-Sheng Huang

  4. Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, 112, Taiwan

    Jong-Ling Fuh

  5. Faculty of Medicine, National Yang-Ming University, Taipei, 112, Taiwan

    Jong-Ling Fuh

  6. Department of Nuclear Medicine, Cheng-Hsin General of Medicine Hospital, Taipei, 112, Taiwan

    Ren‑Shyan Liu

  7. Department of Radiology, Taipei Medical University-Shuang Ho Hospital, New Taipei, 235, Taiwan

    Cheng-Han Wu

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  1. Bang-Hung Yang
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Correspondence to Cheng-Han Wu.

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Yang, BH., Chen, JC., Chou, WH. et al. Classification of Alzheimer’s Disease from 18F-FDG and 11C-PiB PET Imaging Biomarkers Using Support Vector Machine. J. Med. Biol. Eng. 40, 545–554 (2020). https://doi.org/10.1007/s40846-020-00548-1

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  • DOI: https://doi.org/10.1007/s40846-020-00548-1

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