深度学习进行肝纤维化分期：模型诊断决策的可视化解释。
Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.
机构信息
Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.
出版信息
Eur Radiol. 2021 Dec;31(12):9620-9627. doi: 10.1007/s00330-021-08046-x. Epub 2021 May 20.
OBJECTIVES
Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning.
METHODS
The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage.
RESULTS
The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4).
CONCLUSIONS
Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms.
KEY POINTS
• Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.
目的
深度学习已被证明能够基于增强 CT 图像对肝纤维化进行分期。然而，到目前为止，该算法仍被视为黑盒，缺乏透明度。本研究旨在为深度学习的诊断决策提供基于视觉的解释。
方法
在 252 例经组织学证实的肝纤维化分期患者的门静脉期增强 CT 图像上开发了肝纤维化分期网络（LFS 网络）。为了对 LFS 网络做出的诊断决策提供可视化解释，使用梯度加权类激活映射（Grad-cam）生成位置图，以指示 LFS 网络在预测肝纤维化分期时关注的位置。
结果
LFS 网络在测试集上对显著纤维化（F2-F4）、进展性纤维化（F3-F4）和肝硬化（F4）的诊断性能的受试者工作特征曲线下面积分别为 0.92、0.89 和 0.88。位置图表明，在无肝纤维化（F0）患者中，LFS 网络更关注肝脏表面，而在肝硬化（F4）患者中，它更关注肝脏和脾脏实质。
结论
深度学习方法能够利用基于 CT 的肝脏表面、肝脏实质和肝外信息来预测肝纤维化分期。因此，我们建议在开发基于深度学习的肝纤维化分期算法时，在 CT 图像上使用整个上腹部。
关键点
深度学习算法可以使用增强 CT 图像对肝纤维化进行分期，但该算法仍被视为黑盒，缺乏透明度。
Gradient-weighted Class Activation Mapping 生成的位置图可以指示肝纤维化分期网络的重点。
深度学习方法使用基于 CT 的肝脏表面、肝脏实质和肝外信息来预测肝纤维化分期。
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