Abstract
ObjectivesDeep 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.MethodsThe 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.ResultsThe 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).ConclusionsDeep 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.
Highlights
In patients with liver fibrosis, normal liver parenchyma is replaced by scar tissue [1]
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
Summary
In patients with liver fibrosis, normal liver parenchyma is replaced by scar tissue [1]. Main causes of liver fibrosis are excessive alcohol use, severe steatosis/steatohepatitis, and viral hepatitis [2]. Cirrhosis is the most severe stage of liver fibrosis, which can lead to portal hypertension, development of hepatocellular carcinoma, and liver failure Eur Radiol important to adequately diagnose and stage liver fibrosis before its progression into irreversible, end-stage liver disease. The current gold standard for liver fibrosis diagnosis and staging is histopathological examination of liver tissue obtained through percutaneous biopsy. Biopsy has several drawbacks, such as peri-procedural pain or discomfort, major hemorrhage with a reported mortality rate up to 1.6%, and the risk of sampling error due to analysis of only a small liver parenchyma specimen [3,4,5]
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