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Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients’ quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is required. We developed a deep-learning (DL)-based signature using positron emission tomography (PET) to objectively evaluate cognitive decline in patients with stroke.

Methods

We built a DL model that differentiated Alzheimer’s disease (AD) from normal controls (NC) using brain fluorodeoxyglucose (FDG) PET from the Alzheimer’s Disease Neuroimaging Initiative database. The model was directly transferred to a prospectively enrolled cohort of patients with stroke to differentiate patients with dementia from those without dementia. The accuracy of the model was evaluated by the area under the curve values of receiver operating characteristic curves (AUC-ROC). We visualized the distribution of DL-based features and brain regions that the model weighted for classification. Correlations between cognitive signature from the DL model and clinical variables were evaluated, and survival analysis for post-stroke dementia was performed in patients with stroke.

Results

The classification of AD vs. NC subjects was performed with AUC-ROC of 0.94 (95% confidence interval [CI], 0.89–0.98). The transferred model discriminated stroke patients with dementia (AUC-ROC = 0.75). The score of cognitive decline signature using FDG PET was positively correlated with age, neutrophil–lymphocyte ratio and platelet-lymphocyte ratio and negatively correlated with body mass index in patients with stroke. We found that the cognitive decline score was an independent risk factor for dementia following stroke (hazard ratio, 10.90; 95% CI, 3.59–33.09; P < 0.0001) after adjustment for other key variables.

Conclusion

The DL-based cognitive signature using FDG PET was successfully transferred to an independent stroke cohort. It is suggested that DL-based cognitive evaluation using FDG PET could be utilized as an objective biomarker for cognitive dysfunction in patients with cerebrovascular diseases.

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Data availability

The datasets from our institution analysed during the current study are available from the corresponding author on reasonable request. The datasets from ADNI are available in the LONI Image and Data Archive (IDA) repository, https://ida.loni.usc.edu.

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Funding

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2018K1A3A1A39087727, NRF2019R1F1A1059455). This research was also funded by the National Research Foundation of Korea (NRF-2019K1A3A1A14065446), the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, Ministry of Food and Drug Safety) (Project Number: 202011A06), and Seoul R&BD Program (BT200151). No other potential conflict of interest relevant to this article was reported.

Author information

Author notes

  1. Reeree Lee and Hongyoon Choi contributed equally to this paper.

Authors and Affiliations

  1. Department of Nuclear Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, 224-1, Heukseok-dong, Dongjak-gu, Seoul, 06974, Republic of Korea

    Reeree Lee & Ju Won Seok

  2. Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea

    Hongyoon Choi

  3. Department of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, 224-1, Heukseok-dong, Dongjak-gu, Seoul, 06974, Republic of Korea

    Kwang-Yeol Park

  4. Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea

    Jeong-Min Kim

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  1. Reeree Lee
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Contributions

Ju Won Seok and Kwang-Yeol Park designed the study. Material preparation and data collection were performed by Reeree Lee and Jeong-Min Kim. Imaging analyses were performed by Reeree Lee and Hongyoon Choi. The first draft of the manuscript was written by Reeree Lee and Hongyoon Choi. Data interpretation and critical revision of the manuscript were performed by Ju Won Seok and Kwang-Yeol Park. All authors contributed to writing manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Kwang-Yeol Park or Ju Won Seok.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Institutional Review Board of Chung-Ang University Hospital (C2015061) and all subjects signed an informed consent form.

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Patients signed informed consent regarding publishing their data and photographs.

Clinical trial registration

Registration no.: KCT0002462, date of registration: 09/11/2017, retrospectively registered.

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This article is part of the Topical Collection on Neurology

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Lee, R., Choi, H., Park, KY. et al. Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach. Eur J Nucl Med Mol Imaging 49, 1254–1262 (2022). https://doi.org/10.1007/s00259-021-05556-0

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