Wilder-Smith Adrian Jonathan, Yang Shan, Weikert Thomas, Bremerich Jens, Haaf Philip, Segeroth Martin, Ebert Lars C, Sauter Alexander, Sexauer Raphael
Division of Research and Analytical Services, University Hospital Basel, 4031 Basel, Switzerland.
Department of Radiology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland.
Diagnostics (Basel). 2022 Apr 21;12(5):1045. doi: 10.3390/diagnostics12051045.
Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016−01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48−99.38%) and 100.00% (95% CI 96.38−100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904−0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.
心包积液（PEF）在计算机断层扫描（CT）中常常被漏诊，这对出现血流动力学障碍的患者的治疗结果有特别大的影响。一种自动的PEF检测、分割和分类工具将加快并改善基于CT的PEF诊断；利用放射学报告（2016年1月至2021年1月）识别出258例有（206例为单纯PEF，52例为心包积血）和无PEF的CT（每种各134例有对比剂，124例无增强）。对PEF进行了手动三维分割。在316例病例上训练了一个深度卷积神经网络（nnU-Net），并在其余200例和22例外部尸检CT上分别进行测试。在40例CT上测试了阅片者间的变异性。PEF分类利用每个预测的中位亨氏单位。PEF检测的敏感性和特异性分别为97%（95%CI 91.48−99.38%）和100.00%（95%CI 96.38−100.00%），诊断心包积血的敏感性和特异性分别为89.74%和83.61%（AUC 0.944，95%CI 0.904−0.984）。模型性能（Dice系数：0.75±0.01）不劣于阅片者间的性能（0.69±0.02），且不受对比剂使用和其他胸部病变的影响（p>0.05）。外部数据集测试得到了类似结果。我们的模型在一个复杂的数据集中能可靠地在CT上检测、分割和分类PEF，有可能作为一种警报工具，同时提高报告质量。该模型和相应的数据集可公开获取。
