Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Acute Cardiac Care, Andreas Grüntzig Heart Catheterization Laboratories, Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
Sci Rep. 2020 Nov 25;10(1):20537. doi: 10.1038/s41598-020-76432-4.
Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80-86.2), specificity of 83.7% (CI 75.7-92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83-0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.
心脏磁共振（CMR）成像已成为非侵入性诊断心肌梗死后心肌病（TTS）的重要技术。CMR 成像在 TTS 中的长期预后价值尚未完全阐明。本研究旨在使用机器学习评估基于 CMR 图像的纹理分析（TA）在 TTS 患者中的预后价值。在这项多中心研究（InterTAK 注册研究）中，我们研究了 58 例 TTS 患者（56 例女性，平均年龄 68±12 岁）的 CMR 成像数据。CMR 成像在 TTS 症状发作后 4 天的急性期至亚急性期进行。使用左心室短轴晚期钆增强和 T2 加权（T2w）图像的自由手感兴趣区进行 TA。共纳入 608 个 TA 特征，外加年龄、性别和体重指数等参数。通过去除 ICC≤0.6 的 TA 特征和冗余的 TA 特征（Pearson 相关系数 r>0.8 的相关矩阵）进行降维。应用五种常用的机器学习分类器（人工神经网络多层感知器、决策树 J48、朴素贝叶斯、随机森林和序贯最小优化）进行十折交叉验证，以评估包括主要不良心脏和脑血管事件（MACCE）在内的 5 年预后。降维后得到 10 个具有预后信息的 TA 特征，均基于 T2w 图像。朴素贝叶斯机器学习分类器的总体性能最佳，其敏感性为 82.9%（置信区间[CI] 80-86.2），特异性为 83.7%（CI 75.7-92），受试者工作特征曲线下面积为 0.88（CI 0.83-0.92）。这是第一项确定可预测 TTS 患者长期预后的独特 T2w 衍生 TA 特征的验证性研究。这些特征可能成为 TTS 患者的影像学预后生物标志物。
