Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Department of Radiology, University of Calgary, Calgary, Canada.
PLoS One. 2020 Nov 5;15(11):e0241917. doi: 10.1371/journal.pone.0241917. eCollection 2020.
An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.
Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.
Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.
The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.
准确预测急性缺血性脑卒中患者的组织转归对于治疗决策具有重要意义。迄今为止，已经提出了各种机器学习模型，这些模型旨在结合多参数成像数据实现这一目标。然而，这些机器学习模型中的大多数都是使用从整个大脑中提取的体素信息进行训练的，没有考虑到脑区之间存在的对缺血的易感性差异。本研究的目的是开发和评估一种局部组织转归预测方法，该方法使用局部训练的机器学习模型进行预测，从而考虑到区域差异。
使用 99 例急性缺血性脑卒中患者的多参数 MRI 数据来开发和评估局部组织转归预测方法。为每位患者的弥散（ADC）和灌注参数图（CBF、CBV、MTT、Tmax）及其相应的随访病变掩模进行配准，以匹配 MNI 脑图谱。使用局部方法（使用局部数据分别为每个特定体素位置训练模型的方法）和全局方法（使用大脑所有体素训练的单个模型的方法）训练逻辑回归（LR）和随机森林（RF）模型。比较使用全局和局部 RF 和 LR 模型以及混合（混合）方法得到的组织转归预测，并使用受试者工作特征曲线（ROC AUC）、Dice 系数以及灵敏度和特异性度量值对其进行定量评估和比较。
统计分析显示，混合方法的 ROC AUC 和 Dice 值最高。LR 的 ROC AUC 值为 0.872，RF 的 Dice 值为 0.353，这些值显著高于其他两种方法（p <0.01）。此外，局部方法的灵敏度最高，为 0.448（LR）。总的来说，混合方法仅在灵敏度（LR）方面优于局部方法，在特异性方面优于其他两种方法。然而，在这些情况下，效应大小相对较小。
本研究结果表明，与使用大脑所有体素信息独立于位置的单个全局机器学习模型相比，使用局部训练的机器学习模型可以得到更好的病变转归预测结果。
