The Application Of CT Radiomics In Predicting Future Ischemic Strokes In Patients With Silent Lacunar Infarction

Objective:This study aimed to investigate the integration of radiomics and clinical factors in cranial computed tomography?CT?to predict ischemic strokes in patients with silent lacunar infarction?SLI?.Methods:One hundred forty-eight eligible patients diagnosed with SLI according to their baseline cranial CT findings were included,and radiomic features were extracted for each patient.A least absolute shrinkage and selection operator?LASSO?–Cox regression analysis was used to select significant prognostic factors,based on which we constructed Model^C with clinical factors,Model^R with radiomic features,and Model^CR with both factors.The Kaplan–Meier method was used to compare stroke-free survival probabilities in the training cohort and the validation cohort.A nomogram and a calibration curve were used for further evaluation.Results:Radiomic signature,age,dyslipidemia,and multiple infarctions were independently associated with future ischemic strokes?P<0.01,P=0.09,P=0.03,and P=0.02,respectively?.Model^CR had the best accuracy,with 6-,12-,and 18-month areas under the curve of 0.84,0.81,and 0.79 for the training cohort and 0.79,0.88,and 0.75 for the validation cohort,respectively.Patients with a Model^CR score<0.17 had higher probabilities of stroke-free survival.The prognostic nomogram of the training and validation cohorts showed acceptable discrimination and calibration capability?concordance index[95%confidence interval],0.7864[0.70–0.86],and0.7140[0.59–0.83],respectively?.Conclusions:Radiomic analyses based on baseline CT images might provide a novel approach for predicting future ischemic strokes in patients with SLI.Older patients and those with dyslipidemia and multiple infarctions are at high risk for ischemic stroke and require close monitoring and intensive intervention in clinical practice.