Clinical-radiomics combined model in prediction of early hematoma expansion after spontaneous intracerebral hemorrhage
10.3760/cma.j.cn115354-20210402-00217
- VernacularTitle:临床-影像组学联合模型预测自发性脑出血后早期血肿扩大的研究
- Author:
Yuanyuan CHEN
1
;
Zhiming ZHOU
;
Shike WANG
;
Zuhua SONG
;
Dajing GUO
Author Information
1. 重庆医科大学附属第二医院放射科，重庆 400010
- Keywords:
Spontaneous intracerebral hemorrhage;
Hematoma expansion;
Radiomics;
Predictive model;
Nomogram
- From:
Chinese Journal of Neuromedicine
2021;20(11):1117-1123
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the risk factors for early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH), and construct a clinical-radiomics combined model to predict HE after sICH.Methods:From April 2014 to September 2020, 339 patients with sICH who underwent plain CT scans in Radiology Department of our hospital were recruited. Patients were divided into HE group and non-HE group according to whether HE occurred (HE was defined as an increase in hematoma volume>33% or 6 mL on the follow-up CT within 24 h). The clinical data of non-HE group and HE group were compared, and multivariate Logistic regression analysis was used to detect independent risk factors for HE. The radiomics features were extracted from the regions of interest of the hematoma in the first CT scan images; the optimal radiomics features were selected using least absolute shrinkage and selection operator (LASSO) regression model and 10-fold cross-validation method, and then, the radiomics scores (R-score) were calculated; the risk factors for HE (clinical data) and R-score (radiomics data) were used to construct the clinical model, R-score model, and clinical-radiomics combined model; receiver operating characteristic (ROC) curve was performed to evaluate the prediction performance of clinical model, R-score model, and clinical-radiomics combined model; the best model was visualized as a nomogram and a calibration curve was drawn to evaluate the prediction accuracy of this model.Results:As compared with patients in the non-HE group, patients in the HE group had shorter time from sICH onset to first CT, higher percentage of patients with diabetes, lower platelet count, lower Glasgow Coma Scale (GCS) scores, and larger baseline hematoma volume in CT image, with significant differences ( P<0.05). Multivariate Logistic regression analysis showed that baseline hematoma volume ( OR=1.015, 95%CI: 1.000-1.030, P=0.046), GCS scores ( OR=0.914, 95%CI: 0.839-0.995, P=0.039), time from sICH onset to first CT ( OR=0.855, 95%CI: 0.741-0.987, P=0.032), and diabetes ( OR=0.522, 95%CI: 0.311-0.875, P=0.014) were independent risk factors for HE. By using LASSO regression and 10-fold cross-validation method, 20 optimal radiomics features were finally selected. The area under ROC curve of clinical model, R-score model, and clinical-radiomics combined model were 0.650, 0.860, and 0.870, respectively. The calibration curve showed that the prediction accuracy of clinical-radiomics combined model in early HE had good consistency with the actual occurrence probability. Conclusion:The clinical-radiomics combined model could effectively predict early HE with good calibration, which is helpful in individualized clinical assessment of risk of early HE in SICH patients.
