Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage

作者全名："Song, Zuhua; Guo, Dajing; Tang, Zhuoyue; Liu, Huan; Li, Xin; Luo, Sha; Yao, Xueying; Song, Wenlong; Song, Junjie; Zhou, Zhiming"

作者地址："[Song, Zuhua; Guo, Dajing; Li, Xin; Luo, Sha; Yao, Xueying; Song, Wenlong; Song, Junjie; Zhou, Zhiming] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 2, 74 Linjiang Rd, Chongqing 400010, Peoples R China; [Tang, Zhuoyue] Chongqing Gen Hosp, Dept Radiol, Chongqing, Peoples R China; [Liu, Huan] GE Healthcare, Shanghai, Peoples R China"

通信作者："Zhou, ZM (corresponding author), Chongqing Med Univ, Dept Radiol, Affiliated Hosp 2, 74 Linjiang Rd, Chongqing 400010, Peoples R China."

来源：KOREAN JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000621835100012

JCR分区：Q1

影响因子：4.8

年份：2021

卷号：22

期号：3

开始页：415

结束页：424

文献类型：Article

关键词：Hematoma expansion; Radiomics; Machine learning; Intracerebral hemorrhage; Computed tomography

摘要："Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE."

基金机构："medical research Key Program of the combination of Chongqing National health commission; Chongqing science and technology bureau, China [2019ZDXM010]; Basic and Frontier Research Project of Chongqing, China [cstc2016jcyjA0294]"

基金资助正文："This study was supported by the medical research Key Program of the combination of Chongqing National health commission and Chongqing science and technology bureau, China (no 2019ZDXM010); the Basic and Frontier Research Project of Chongqing, China (no cstc2016jcyjA0294)."