School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
Department of Critical Care Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Comput Math Methods Med. 2023 Feb 2;2023:6403556. doi: 10.1155/2023/6403556. eCollection 2023.
The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone.
Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model.
The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set.
The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone.
本研究旨在评估基于 MRI 的纹理分析与机器学习相结合在区分脓毒症相关性脑病（SAE）与单纯脓毒症中的性能。
收集了 1 例 SAE 患者和 125 例单纯脓毒症患者的 66 个 MRI-T1WI 图像。选择额叶、脑干、海马体和杏仁核作为感兴趣区（ROI）。使用 MaZda 软件获取每个 ROI 的 279 个纹理特征。降维后，采用 CatBoost 模型对每个 ROI 的 30 个高判别特征进行分析，以区分 SAE 与单纯脓毒症。
构建了额叶、脑干、海马体和杏仁核的分类模型。在验证集中，分类准确率均高于 0.83，曲线下面积（AUC）均超过 0.90。
不同解剖部位的 SAE 患者与单纯脓毒症患者的纹理特征存在差异，提示基于 MRI 的纹理分析与机器学习有助于区分 SAE 与单纯脓毒症。
