Haider Stefan P, Qureshi Adnan I, Jain Abhi, Tharmaseelan Hishan, Berson Elisa R, Zeevi Tal, Werring David J, Gross Moritz, Mak Adrian, Malhotra Ajay, Sansing Lauren H, Falcone Guido J, Sheth Kevin N, Payabvash Seyedmehdi
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.
Department of Otorhinolaryngology, University Hospital of Ludwig-Maximilians-Universität München, Munich, Germany.
Front Neurosci. 2023 Aug 16;17:1225342. doi: 10.3389/fnins.2023.1225342. eCollection 2023.
To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH).
Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort ( = 449) and confirmed their performance in an independent validation cohort ( = 448). In addition to = 1,130 radiomic features, = 6 clinical variables associated with HE, = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance.
A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical ( = 0.02, = 0.01) and visual signature ( = 0.03, = 0.01) as well as the BAT score ( < 0.001, < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly ( < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance.
Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.
基于脑出血（ICH）患者入院时的非增强头部计算机断层扫描（CT），设计并验证即将发生血肿扩大（HE）的放射组学特征。
利用一个大型多中心高血压性幕上自发性ICH患者临床试验数据集，我们在一个发现队列（n = 449）中开发了预测HE的特征，并在一个独立验证队列（n = 448）中确认了它们的性能。除了1130个放射组学特征、6个与HE相关的临床变量、8个先前定义的HE视觉标志物、BAT评分及其组合作为特征的候选变量集外。受试者操作特征曲线（AUC）下的面积量化了特征的性能。
一个结合了选定放射组学特征和临床变量的特征在发现队列和独立验证队列中分别获得了最高的AUC（95%置信区间），分别为0.67（0.61 - 0.72）和0.64（0.59 - 0.70），显著优于临床（p = 0.02，p = 0.01）和视觉特征（p = 0.03，p = 0.01）以及BAT评分（p < 0.001，p < 0.001）。将视觉标志物添加到放射组学特征中未能提高预测性能。所有特征与3个月时的功能结局显著相关（p < 0.001），强调了它们的预后相关性。
ICH患者入院时非增强头部CT的放射组学特征可以预测即将发生的HE，具有稳定的可推广性；将放射组学与临床预测指标相结合产生了最高的预测价值。通过在未来的临床试验中对HE风险升高的患者进行选择性抗扩张治疗，所提出的标志物可能会提高治疗效果，并最终改善结局。
