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| The prediction value of early hematoma expansion after intracerebral hemorrhage based on clinical features and noncontrast CT radiomic feature models |
| XU Lei, GE Huaizhi, ZHANG Zhijing, CHEN Bo,CHENG Jianmin, WU Aiqin |
| Department of Radiology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325027, China |
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| Cite this article: |
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XU Lei,GE Huaizhi,ZHANG Zhijing, et al. The prediction value of early hematoma expansion after intracerebral hemorrhage based on clinical features and noncontrast CT radiomic feature models[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2021, 51(10): 787-792.
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Abstract Objective: To investigate the predicting value of early hematoma expansion after intracerebral hemorrhage based on clinical and non-contrast CT radiomic feature machine learning models. Methods: A total of 261 cases of acute early spontaneous intracerebral hemorrhage from the Second Affiliated Hospital of Wenzhou
Medical University from January 2018 to May 2020 were collected. Patients were assigned as hematoma expansion group and non-hematoma expansion group according to the presence of early hematoma expansion. All samples were divided into training set (182) and testing set (79) randomly according to ratio of 7:3. Regions of interest of lesions were delineated by 3D Sicer software. Radiomic features were extracted and clinical features (demographic and CT imaging features) of each patient were collected. Least absolute shrinkage and selection operator (LASSO) was used to select radiomic features. Univariate analysis and multivariate logistic regression analysis were used to select clinically predictive independent risk factors. Logistic regression models for predicting invasiveness of pulmonary adenocarcinoma were established based on clinical features, radiomic features and clinical features combined with radiomic features. Receiver operating characteristic curve andarea under curve were used to evaluate the predictive performance of models. Results: A total of 396 radiomic features were extracted from CT images, of which 7 radiomic features with discriminative significance were selected after dimensionality reduction by least absolute shrinkage and selection operator (LASSO). A total of 10 clinical features were collected, and swirl sign, black hole sign and irregular shape were found to be independent risk factors for predicting HE after univariate analysis and multivariate Logistic regression analysis (P<0.05).Predictive performance of the radiomic feature model, clinical feature model and combined model were as follows: in the training set, AUC was 0.924, 0.836 and 0.968, specificity was 91.4%, 81.0% and 95.2%, sensitivity was 81.8%, 78.4% and 84.4%; in the testing set, AUC was 0.919, 0.796 and 0.929, specificity was 81.8%, 77.5% and 88.1%, sensitivity was 76.1%, 64.5% and 80.4%. Conclusion: Logistic regression model based on clinical and CT radiomic features has certain predictive efficiency for early hematoma expansion after intracerebral hemorrhage.
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Received: 15 April 2021
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