CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage

医学 神经组阅片室 放射科 无线电技术 血肿 格拉斯哥昏迷指数 脑出血 放射性武器 接收机工作特性 内科学 外科 神经学 精神科
作者
Fei Yu,Mingguang Yang,Cheng He,Yanli Yang,Ying Peng,Hua Yang,Hong Lü,Heng Liu
出处
期刊:European Radiology [Springer Science+Business Media]
卷期号:35 (1): 6-19 被引量:3
标识
DOI:10.1007/s00330-024-10921-2
摘要

Abstract Objectives This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. Methods A retrospective continuous collection of HICH patients from three medical centers was divided into a training set ( n = 555), a validation set ( n = 239), and a test set ( n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). Results In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. Conclusion The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. Clinical relevance statement CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. Key Points HE is an important prognostic factor in patients with HICH . The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively . This model provides a tool for a personalized clinical assessment of early HE risk .
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