Ironside Natasha, El Naamani Kareem, Rizvi Tanvir, Shifat-E-Rabbi Mohammad, Kundu Shinjini, Becceril-Gaitan Andrea, Pas Kristofor, Snyder Harrison, Chen Ching-Jen, Langefeld Carl D, Woo Daniel, Mayer Stephan, Connolly E Sander, Rohde Gustavo
Department of Neurological Surgery, University of Virginia Health System, Charlottesville, VA.
Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, PA.
medRxiv. 2025 Sep 2:2024.05.14.24307384. doi: 10.1101/2024.05.14.24307384.
Hematoma expansion is a consistent predictor of poor neurological outcome and mortality after spontaneous intracerebral hemorrhage (ICH). An incomplete understanding of its biophysiology has limited early preventative intervention. Transport-based morphometry (TBM) is a mathematical modeling technique that uses a physically meaningful metric to quantify and visualize discriminating image features that are not readily perceptible to the human eye. We hypothesized that TBM could discover relationships between hematoma morphology on initial Non-Contrast Computed Tomography (NCCT) and hematoma expansion. 170 spontaneous ICH patients enrolled in the multi-center international Virtual International Trials of Stroke Archive (VISTA-ICH) with time-series NCCT data were used for model derivation. Its performance was assessed on a test dataset of 170 patients from the Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH) study. A unique transport-based representation was produced from each presentation NCCT hematoma image to identify morphological features of expansion. The principal hematoma features identified by TBM were larger size, density heterogeneity, shape irregularity and peripheral density distribution. These were consistent with clinician-identified features of hematoma expansion, corroborating the hypothesis that morphological characteristics of the hematoma promote future growth. Incorporating these traits into a multivariable model comprising morphological, spatial and clinical information achieved a AUROC of 0.71 for quantifying 24-hour hematoma expansion risk in the test dataset. This outperformed existing clinician protocols and alternate machine learning methods, suggesting that TBM detected features with improved precision than by visual inspection alone. This pre-clinical study presents a quantitative and interpretable method for discovery and visualization of NCCT biomarkers of hematoma expansion in ICH patients. Because TBM has a direct physical meaning, its modeling of NCCT hematoma features can inform hypotheses for hematoma expansion mechanisms. It has potential future application as a clinical risk stratification tool.
血肿扩大是自发性脑出血（ICH）后神经功能预后不良和死亡率的一致预测指标。对其生物物理学的不完全理解限制了早期预防性干预。基于传输的形态计量学（TBM）是一种数学建模技术，它使用具有物理意义的指标来量化和可视化人眼不易察觉的有区别的图像特征。我们假设TBM可以发现初始非增强计算机断层扫描（NCCT）上的血肿形态与血肿扩大之间的关系。170例纳入多中心国际卒中虚拟试验数据库（VISTA-ICH）且有时间序列NCCT数据的自发性ICH患者用于模型推导。在来自脑出血种族/民族差异（ERICH）研究的170例患者的测试数据集上评估其性能。从每个呈现的NCCT血肿图像生成独特的基于传输的表示，以识别扩大的形态特征。TBM识别的主要血肿特征是更大的尺寸、密度异质性、形状不规则和周边密度分布。这些与临床医生确定的血肿扩大特征一致，证实了血肿形态特征促进未来生长的假设。将这些特征纳入包含形态、空间和临床信息的多变量模型中，在测试数据集中量化24小时血肿扩大风险的曲线下面积（AUROC）为0.71。这优于现有的临床医生方案和其他机器学习方法，表明TBM检测到的特征比仅通过视觉检查具有更高的精度。这项临床前研究提出了一种定量且可解释的方法，用于发现和可视化ICH患者血肿扩大的NCCT生物标志物。由于TBM具有直接的物理意义，其对NCCT血肿特征的建模可为血肿扩大机制的假设提供信息。它有作为临床风险分层工具的潜在未来应用。
