Molecular and Statistical Biophysics Group, International School for Advanced Studies (SISSA), Via Bonomea 265, 34136, Trieste, Italy.
Dipartimento di Matematica, Informatica e Geoscienze, Università degli studi di Trieste, via Valerio 12/1, 34127, Trieste, Italy.
Neurol Sci. 2024 Jul;45(7):3245-3253. doi: 10.1007/s10072-024-07339-5. Epub 2024 Jan 29.
ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome.
We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients.
In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases, the volume marker provides supplementary and independent information. Indeed, the clinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10.
The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.
ASPECTS 是一种广泛用于在非增强 CT (NECT)上识别早期卒中征象的标志物,但它存在个体间的差异,并且可能难以被非专业人员使用。我们引入了一种能够自动估计 3D 空间中早期急性缺血性变化NECT 体积扩展的算法。我们比较了该标志物与经验丰富的医生评估的 ASPECTS 预测临床结局的能力。
我们分析和处理了 153 名因急性缺血性卒中入院的患者的神经影像学数据。所有患者均在入院时和随访时进行了NECT。所开发的算法基于对两个半球图像灰度的自动比较来识别早期缺血性低密区,假设在健康患者中,这两个半球图像互为镜像。
在用于估计 ASPECTS 的两个标准轴位切片中,算法识别的区域与经验丰富的医生识别的区域有很大的重叠。然而,在许多患者中,自动识别的区域会显著扩展到其他切片。在这些情况下,体积标志物提供了补充和独立的信息。事实上,体积标志物为 0 的患者的临床结局可以比 ASPECTS 为 10 的患者的结局具有更高的统计置信度。
我们的算法自动识别的急性缺血区域在 3D 空间中的体积扩展和位置与由专家医生评估的 ASPECTS 值提供的数据大多是一致的,并且在某些情况下,是互补和独立的。