The value of a combined model of clinical factors and non-contrast CT radiomics in predicting symptomatic hemorrhagic transformation after intravenous thrombolysis in patients with anterior circulation ischemic stroke
10.3760/cma.j.cn112149-20240115-00026
- VernacularTitle:临床因素联合平扫CT影像组学预测前循环缺血性脑卒中静脉溶栓后症状性出血转化的价值
- Author:
Dandan JI
1
;
Tianle WANG
;
Li ZHU
;
Yu LU
;
Xiwu RUAN
Author Information
1. 南通市第一人民医院 南通大学第二附属医院影像科，南通 226001
- Keywords:
Stroke;
Symptomatic intracranial haemorrhage;
Tomography, X-ray computed;
Radiomics
- From:
Chinese Journal of Radiology
2024;58(10):1021-1027
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the efficacy of a combined model constructed by the radiomics features based on non-contrast CT (NCCT) combined with clinical risk factors in predicting the occurrence of symptomatic intracranial hemorrhagic transformation (sICH) after intravenous thrombolysis with recombinant tissue plasminogen activator (rt-PA) in patients with anterior circulation acute ischemic stroke (AIS).Methods:In this cross-sectional study, clinical and imaging data of 316 patients with anterior circulation AIS who received intravenous thrombolysis with rt-PA at Nantong First People′s Hospital from October 2018 to September 2022 were retrospectively analyzed. The cases were divided into a training set of 210 cases and a validation set of 106 cases by stratified random sampling at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were performed to select the independent clinical risk factors for predicting sICH. The infarct area was delineated on the NCCT images and radiomics features were extracted. The extracted radiomics features were dimensionally reduced and selected using the inter-and intra-group correlation coefficients, maximum correlation and minimum redundancy, and the least absolute shrinkage and selection operator, and then the radiomics score was calculated. Finally, multivariate logistic analysis was performed and the clinical risk factors and radiomics scores were used to establish the clinical model, the radiomics model and the radiomics-clinical combined model. The predictive efficacy of each model was evaluated by the receiver operating characteristic curve and the area under the curve, and decision curve analysis (DCA) was used to calculate and quantify the net benefits of each predictive model.Results:In total eight radiomics features were selected to construct the radiomics model. Multivariate logistic analysis showed that hypertension ( OR=2.703, 95% CI 1.153-6.334, P=0.022), atrial fibrillation ( OR=3.023, 95% CI 1.290-7.085, P=0.011), and the National Institutes of Health Stroke Scale score at admission ( OR=1.078, 95% CI 1.017-1.143, P=0.012) were independent risk factors for sICH after rt-PA intravenous thrombolysis in patients with anterior circulation AIS. In the validation set, the area under the curve of the combined model for predicting sICH was 0.763 (95% CI 0.618-0.909), which was higher than that of the clinical model 0.710 (95% CI 0.552-0.868) and the radiomics model 0.708 (95% CI 0.568-0.848). DCA showed that the combined model could allow patients to obtain higher net benefits. Conclusion:The combined model constructed based on the radiomics of NCCT and clinical risk factors has a high diagnostic efficacy in predicting sICH after rt-PA intravenous thrombolysis in patients with anterior circulation AIS.
