Hokkinen Lasse, Mäkelä Teemu, Savolainen Sauli, Kangasniemi Marko
Radiology, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki 00290, Finland.
Department of Physics, University of Helsinki, Helsinki 00014, Finland.
BJR Open. 2024 Jan 5;6(1):tzae001. doi: 10.1093/bjro/tzae001. eCollection 2024 Jan.
CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods.
The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed.
The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (=0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (=0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121).
This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation.
CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.
基于CT血管造影（CTA）的机器学习方法用于梗死体积估计时，有高估梗死核心和最终梗死体积（FIV）的趋势。我们的目的是评估影响这些方法可靠性的因素。
基于Miteff系统和低灌注强度比（HIR），使用Pearson相关系数和中位数体积，评估121例前循环急性缺血性卒中患者侧支循环对卷积神经网络（CNN）估计值与FIV之间相关性的影响。还评估了成功与无效血栓切除术之间的相关性。分析了个体CTA与CT灌注研究的时间关系。
根据Miteff系统或HIR评估，CNN估计体积与FIV之间的相关强度不会因侧支状态而显著变化，相关性较差至中等（=0.09 - 0.50）。在无效血栓切除术患者中发现最强的相关性（=0.61）。与FIV相比，CNN中位数估计值有高估趋势。几乎所有患者（120/121）的CTA均在动脉中期采集。
本研究表明侧支状态对CNN可靠性无影响，在无效血栓切除术患者中发现最佳相关性。几乎所有患者在动脉中期进行CTA检查可以解释梗死体积高估现象。
CTA时间似乎是影响当前基于CTA的机器学习方法可靠性的最重要因素，强调了优化CTA方案以估计梗死核心的必要性。
