Chen Lingxu, Wang Xiaochen, Wang Sihui, Zhao Xuening, Yan Ying, Yuan Mengyuan, Sun Shengjun
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119, South Fourth Ring West Road, Fengtai District, Beijing, 100070, P.R. China.
Department of Radiology, Beijing Neurosurgical Institute, No.119, South Fourth Ring West Road, Fengtai District, Beijing, 100070, P.R. China.
BMC Med Imaging. 2025 May 23;25(1):182. doi: 10.1186/s12880-025-01722-0.
Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor prognosis and high mortality. This study developed a non-contrast CT (NCCT)-based radiomics nomogram for early DCI prediction in aSAH patients.
Three hundred seventy-seven aSAH patients were included in this retrospective study. Radiomic features from the baseline CTs were extracted using PyRadiomics. Feature selection was conducted using t-tests, Pearson correlation, and Lasso regression to identify those features most closely associated with DCI. Multivariable logistic regression was used to identify independent clinical and demographic risk factors. Eight machine learning algorithms were applied to construct radiomics-only and radiomics-clinical fusion nomogram models.
The nomogram integrated the radscore and three clinically significant parameters (aneurysm and aneurysm treatment and admission Hunt-Hess score), with the Support Vector Machine model yielding the highest performance in the validation set. The radiomics model and nomogram produced AUCs of 0.696 (95% CI: 0.578-0.815) and 0.831 (95% CI: 0.739-0.923), respectively. The nomogram achieved an accuracy of 0.775, a sensitivity of 0.750, a specificity of 0.795, and an F1 score of 0.750.
The NCCT-based radiomics nomogram demonstrated high predictive performance for DCI in aSAH patients, providing a valuable tool for early DCI identification and formulating appropriate treatment strategies.
Not applicable.
迟发性脑缺血（DCI）是动脉瘤性蛛网膜下腔出血（aSAH）后的一种严重并发症，导致预后不良和高死亡率。本研究开发了一种基于非增强CT（NCCT）的放射组学列线图，用于预测aSAH患者的早期DCI。
本回顾性研究纳入了377例aSAH患者。使用PyRadiomics从基线CT中提取放射组学特征。采用t检验、Pearson相关性分析和Lasso回归进行特征选择，以识别与DCI最密切相关的特征。多变量逻辑回归用于识别独立的临床和人口统计学风险因素。应用八种机器学习算法构建仅放射组学和放射组学-临床融合列线图模型。
该列线图整合了放射学评分和三个具有临床意义的参数（动脉瘤及动脉瘤治疗情况和入院时Hunt-Hess评分），支持向量机模型在验证集中表现最佳。放射组学模型和列线图的曲线下面积（AUC）分别为0.696（95%CI：0.578-0.815）和0.831（95%CI：0.739-0.923）。该列线图的准确率为0.775，灵敏度为0.750，特异度为0.795，F1评分为0.750。
基于NCCT的放射组学列线图在预测aSAH患者的DCI方面表现出较高的性能，为早期识别DCI和制定适当的治疗策略提供了有价值的工具。
不适用。
