Yuval Wiesel, Michael Findler, Jonathan Naftali, Asaf Honig, Rani Barnea, Eitan Auriel
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引用次数: 0
Abstract
Background and purpose: Hematoma expansion (HE) is a major cause of early neurologic deterioration and poor outcome in spontaneous intracerebral hemorrhage. The perihematomal region (PHR), a potential site of secondary vascular injury, remains poorly characterized. We evaluated whether quantitative PHR features provide predictive information for HE beyond hematoma features and insight into HE dynamics.
Materials and methods: In this retrospective 2-center study, 149 patients with acute intracerebral hemorrhage underwent baseline CT within 24 hours of symptom onset and follow-up CT within 72 hours. HE was defined as >6 mL or >33% hematoma growth on follow-up CT. Hematoma regions and PHRs were segmented using relative (half and full radius) and absolute (2, 5, and 10 mm) definitions. Radiomic features were extracted and modeled using multivariable logistic regression. Clinical predictors were added to create combined clinical radiomic models, and performance was evaluated on an independent test set.
Results: HE occurred in 29% (44/149) of patients. The hematoma only radiomic model showed modest discrimination (area under the receiver operating characteristic curve [AUROC] = 0.71; 95% CI, 0.62-0.80). Incorporating PHR features improved performance, with the absolute 5-mm PHR model achieving the highest discrimination (AUROC = 0.91; 95% CI, 0.86-0.95). Adding clinical predictors further enhanced performance (AUROC = 0.94; 95% CI, 0.90-0.97). Key PHR features associated with HE included reduced sphericity, increased texture coarseness, and clustered heterogeneity.
Conclusions: Quantitative PHR characterization was strongly associated with HE and provided incremental value beyond hematoma derived radiomics, with further improvement when combined with clinical predictors. This contrast-free approach may support objective risk stratification, guide targeted interventions, and reduce expansion related morbidity.
背景与目的:血肿扩张(HE)是自发性脑出血(ICH)早期神经功能恶化和预后不良的主要原因。血肿周围区(PHR)是继发性血管损伤的潜在部位,但其特征仍然很差。我们评估了定量PHR特征是否能提供血肿特征和HE动力学之外的预测信息。材料和方法:在这项回顾性双中心研究中,149例急性脑出血患者在症状出现24小时内行基线CT检查,并在72小时内行随访CT检查。HE在随访CT上定义为>6 mL或>33%血肿增长。使用相对(半半径和全半径)和绝对(2、5和10毫米)定义对血肿和血肿周围区域进行分割。利用多变量逻辑回归提取放射学特征并建模。添加临床预测因子创建临床-放射学联合模型,并在独立测试集上评估性能。结果:HE发生率为29%(44/149)。仅血肿的放射学模型显示出适度的区分(AUC=0.71, 95% CI 0.62-0.80)。结合PHR特征提高了性能,绝对5毫米PHR模型获得了最高的识别率(AUC=0.91, 95% CI 0.86-0.95)。加入临床预测因子进一步提高了表现(AUC=0.94, 95% CI 0.90-0.97)。与HE相关的主要PHR特征包括球形度降低、纹理粗糙度增加和聚类异质性。结论:定量PHR表征与HE密切相关,并提供了血肿放射组学之外的增量价值,当与临床预测因子结合时,进一步改善。这种无对比的方法可以支持客观的风险分层,指导有针对性的干预措施,减少扩张相关的发病率。