1. Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach
- Author
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Jeong-Min Kim, Hongyoon Choi, Kwang-Yeol Park, Ju Won Seok, and Reeree Lee
- Subjects
medicine.medical_specialty ,Deep Learning ,Neuroimaging ,Alzheimer Disease ,Fluorodeoxyglucose F18 ,Internal medicine ,Humans ,Medicine ,Dementia ,Cognitive Dysfunction ,Radiology, Nuclear Medicine and imaging ,Cognitive decline ,Stroke ,Fluorodeoxyglucose ,Receiver operating characteristic ,business.industry ,Hazard ratio ,Brain ,Cognition ,General Medicine ,medicine.disease ,Positron-Emission Tomography ,Quality of Life ,Cardiology ,Tomography, X-Ray Computed ,business ,Biomarkers ,medicine.drug - Abstract
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. 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. 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
- Published
- 2021