Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, The Netherlands.
Eur Radiol. 2024 Aug;34(8):5080-5093. doi: 10.1007/s00330-024-10584-z. Epub 2024 Jan 29.
Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT.
We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR).
In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given.
DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed.
White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied.
White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.
急性缺血性脑卒中患者在血管内治疗（EVT）前进行静脉溶栓（IVT）可能会引发颅内出血，从而对患者的预后产生负面影响。使用深度学习（DL）测量脑白质病变（WML）的大小（DL-WML）可能有助于安全指导 IVT 的给药。我们旨在开发、验证和评估 CT 上的 DL-WML 体积与 Fazekas 评分（WML-Faz）作为接受 IVT 后直接接受 EVT 的患者的风险因素和 IVT 效应修饰因子。
我们开发了一种 CT 上 WML 分割的深度学习模型，并通过内部和外部测试集进行了验证。在 MR CLEAN No-IV 试验的事后分析中，我们根据改良 Rankin 量表（mRS）将 DL-WML 体积和 WML-Faz 与症状性颅内出血（sICH）和 90 天功能结局相关联。我们使用 WML 测量值与 IVT 给药之间的乘法交互项来评估 IVT 治疗效果的修饰。回归模型用于报告未经调整和调整后的常见优势比（cOR/acOR）。
共分析了 MR CLEAN No-IV 试验的 516 名患者（男/女，291/225；年龄中位数，71[IQR，62-79]）。DL-WML 体积和 WML-Faz 均与 sICH（DL-WML 体积 acOR，1.78[95%CI，1.17;2.70]；WML-Faz acOR，1.53[95%CI，1.02;2.31]）和 mRS（DL-WML 体积 acOR，0.70[95%CI，0.55;0.87]，WML-Faz acOR，0.73[95%CI 0.60;0.88]）相关。仅在未调整的 IVT 效应修饰分析中，如果给予 IVT，则 WML-Faz 与更多的 sICH 相关（p=0.046）。如果给予 IVT，则两种 WML 测量值均与 mRS 较差无关。
DL-WML 体积和 WML-Faz 与功能结局和 sICH 具有相似的关系。尽管在接受 IVT 的情况下，WML-Faz 更严重的患者可能会发生更多的 sICH，但未观察到更差的功能结局。
急性缺血性脑卒中患者的基线 CT 上的脑白质病变严重程度如果使用深度学习或 Fazekas 评分进行测量，具有相似的预测价值。应进一步研究使用脑白质病变严重程度安全给予静脉溶栓。
脑白质损伤是急性缺血性脑卒中患者颅内出血的一个易感危险因素，但在 CT 上仍然难以测量。使用深度学习测量的 CT 上的脑白质病变体积与症状性颅内出血和功能结局较差具有相似的关联，与 Fazekas 评分相同。需要进行患者水平的荟萃分析，以研究基于脑白质病变严重程度选择静脉溶栓在血管内治疗前的获益。
