Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison

• Published in: Journal of stroke and cerebrovascular diseases Vol. 33; no. 7; p. 107731
• Main Authors: Yang, Wen-Song, Liu, Jia-Yang, Shen, Yi-Qing, Xie, Xiong-Fei, Zhang, Shu-Qiang, Liu, Fang-Yu, Yu, Jia-Lun, Ma, Yong-Bo, Xiao, Zhong-Song, Duan, Hao-Wei, Li, Qi, Chen, Shan-Xiong, Xie, Peng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2024
• Subjects: Cerebral hemorrhage; Computed tomography; Hematoma expansion; Radiomics; CPM; NCCT; KNN; SVM; RHE; Radiomics; AUC; ICH; mRS; CT; Computed tomography; Cerebral hemorrhage; GCS; LASSO; IVH; Hematoma expansion; HE; Cerebral Hemorrhage
• ISSN: 1052-3057; 1532-8511
• DOI: 10.1016/j.jstrokecerebrovasdis.2024.107731

Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.