医学
接收机工作特性
队列
无线电技术
倾向得分匹配
回顾性队列研究
曲线下面积
内科学
人工智能
放射科
计算机科学
作者
Dongqin Zhu,Mingyue Zhang,Q. Li,Jinjin Liu,Yuandi Zhuang,Qian Chen,Chao Chen,Yilan Xiang,Yi Zhang,Yunjun Yang
标识
DOI:10.1016/j.crad.2021.03.003
摘要
AIM
To evaluate the association between perihaematomal radiomics features and haematoma expansion (HE). MATERIALS AND METHODS
Clinical and radiological data were collected retrospectively. The 1:1 propensity score matching (PSM) method was used to balance the difference of baseline characteristics between patients with and without HE. Radiomics features were extracted from the intra- and perihaematomal regions. Top HE-associated features were selected using the minimum redundancy, maximum relevancy algorithm. Support vector machine models were used to predict HE. Predictive performance of radiomics features from different regions was evaluated by receiver operating characteristic curve and confusion matrix-derived metrics. RESULTS
A total of 1,062 patients were enrolled. After PSM analysis, the propensity score-matched cohort (PSM cohort) included 314 patients (HE: n=157; non-HE: n=157). The PSM cohort was distributed into the training (n=218) and the validation cohorts (n=96). The predictive performance of intra- and perihaematomal features were comparable in the training (area under the receiver operating characteristic curve [AUC], 0.751 versus 0.757; p=0.867) and the validation cohorts (AUC, 0.724 versus 0.671; p=0.454). By incorporating intra- and perihaematomal features, the combined model outperformed the single intrahaematomal model in the training cohort (AUC, 0.872 versus 0.751; p<0.001). Decision curve analysis (DCA) further confirmed the clinical usefulness of the combined model. CONCLUSION
Perihaematomal radiomics features can predict HE. The integration of intra- and perihaematomal signatures may provide additional benefit to the prediction of HE.
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