Department of Radiology, University Medical Center Utrecht, Utrecht, 3584CX, The Netherlands.
Image Sciences Institute, University Medical Center Utrecht, Utrecht, 3584CX, The Netherlands.
Eur Radiol. 2022 Sep;32(9):6367-6375. doi: 10.1007/s00330-022-08700-y. Epub 2022 Mar 31.
To compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps.
Patients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods-a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability.
A total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification.
Multivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.
• Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions. • Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis. • A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.
比较单参数阈值法与多变量概率分类法在分析 CT 灌注（CTP）参数图中缺血性卒中区域的应用。
本研究纳入了两项多中心试验中的患者，并根据改良的动脉闭塞病变分级将患者分为两组。采用三种方法生成 CTP 参数图：商业方法（ISP）、块循环奇异值分解（bSVD）和非线性回归（NLR）。随访非对比 CT 定义随访梗死区域。采用受试者工作特征曲线分析确定各参数图的常规阈值。使用将可用 CTP 参数组合成单个概率的逻辑回归模型进行概率分类。
共纳入 225 例 CTP 数据集，分为成功再通组 166 例和持续闭塞组 59 例。与组合成概率值相比，各 CTP 参数的准确性和召回率均较低。使用常规阈值时，估计梗死体积与随访梗死体积之间的中位数差值[四分位距]分别为 29/23/23 [52/50/52]（ISP/bSVD/NLR），使用概率分类时为 4/6/11 [31/25/30]（ISP/bSVD/NLR）。
与基于阈值的 CTP 参数图相比，多变量概率图在估计随访非对比 CT 上的梗死病变方面表现更优。多变量概率方法可能使缺血性卒中区域的分类更协调。
• 将 CTP 参数与逻辑回归模型相结合可提高估计缺血性卒中区域的准确性和召回率。• 基于概率分析的体积能更好地预测随访梗死体积，而基于阈值分析的体积则不然。• 多变量概率方法可能使缺血性卒中区域的分类更协调。
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