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Clinical Article
Prediction of the risk of recurrent ischemic stroke based on intracranial plaque radiomics with traditional biomarkers
WANG Yue  HOU Xiaowen  CHEN Huisheng  TAO Lin 

WANG Y, HOU X W, CHEN H S, et al. Prediction of the risk of recurrent ischemic stroke based on intracranial plaque radiomics with traditional biomarkers[J]. Chin J Magn Reson Imaging, 2023, 14(8): 1-9. DOI:10.12015/issn.1674-8034.2023.08.001.

[Abstract] Objective To develop Cox proportional hazards regression model for prediction of the recurrence risk of non-cardiogenic anterior circulation ischemic stroke during 2-year follow-up based on radiomic approach by extracting texture features from a symptomatic middle cerebral artery (MCA) plaque, and to further evaluate the developed model performance.Materials and Methods In our retrospective study from January 2019 to January 2020, a total of 82 eligible patients with first-ever ischemic stroke and middle cerebral artery >50% luminal stenosis underwent baseline intracranial high-resolution magnetic resonance imaging (HRMRI) followed up for 2 year when recurrent non-cardiogenic ischemic stroke in the territory of MCA served as an endpoint event were finally enrolled in current analyses. HRMRI-based radiomic features were manually extracted from an index MCA plaque using 3D-Slicer software package. Multivariable Cox regression analysis was used to develop the predicting model where multi-dimensional parameters were selected by LASSO (least absolute shrinkage and selection operator) regression analysis, for which the performance was further assessed with respect to its calibration, discrimination.Results Of which, 19 cases with endpoint events occurred during the 2-year follow-up period with 13.9 per 100 person-years of the recurrence rate of ischemic stroke. Multivariable Cox regression included top 4 parameters with nonzero coefficients defined by logλmin of LASSO regression (i.e., second-order texture feature, plaque hemorrhage, entropy and low density lipoprotein cholesterol). In the prediction model adjusting for baseline covariants, the gray level co-occurrence matrix was found to be the major contributor to the event endpoint [adjusted hazard ratio (aHR): 5.379, 95% confidence interval (CI): 1.716-16.859, P=0.004, weight=40.23%]. However, plaque hemorrhage (aHR: 2.226, 95% CI: 0.821-6.040, P=0.116, weight=20.86%), entropy (aHR: 1.324, 95% CI: 0.769-2.278, P=0.311, weight=16.13%) and low density lipoprotein cholesterol (aHR: 1.485, 95% CI: 0.877-2.516, P=0.142, weight=22.78%) just showed a trend towards significance. Additionally, the developed prediction model showed a good discrimination with a C-index of 0.8296 and good calibration.Conclusions The findings suggest that our developed prediction model can target a potential sub-population at high risk of recurrent ischemic stroke in which gray level co-occurrence matrix may account for the major contributing, although this must be confirmed in future.
[Keywords] ischemic stroke;atherosclerosis;intracranial stenosis plaques;radiomics;magnetic resonance imaging

WANG Yue1   HOU Xiaowen2   CHEN Huisheng1   TAO Lin3*  

1 Department of Neurology, General Hospital of Northern Theater Command, Shenyang 110000, China

2 School of Public Health, Shenyang Medical College, Shenyang 110034, China

3 Shuren International School, Shenyang Medical College, Shenyang 110034, China

Corresponding author: Tao L, E-mail:

Conflicts of interest   None.

Received  2022-10-19
Accepted  2023-07-21
DOI: 10.12015/issn.1674-8034.2023.08.001
WANG Y, HOU X W, CHEN H S, et al. Prediction of the risk of recurrent ischemic stroke based on intracranial plaque radiomics with traditional biomarkers[J]. Chin J Magn Reson Imaging, 2023, 14(8): 1-9. DOI:10.12015/issn.1674-8034.2023.08.001.

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