Angular Margin Constrained Loss for Automatic Liver Fibrosis Staging

Author(s):  
Katsuhiro Nakai ◽  
Xu Qiao ◽  
Xian-Hua Han
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  

Abstract 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.


Author(s):  
Carole Vitellius ◽  
Anita Paisant ◽  
Adrien Lannes ◽  
Julien Chaigneau ◽  
Frédéric Oberti ◽  
...  

2019 ◽  
Vol 70 (1) ◽  
pp. e781-e782
Author(s):  
Davide Roccarina ◽  
Laura Iogna Prat ◽  
Marta Guerrero ◽  
Elena Buzzetti ◽  
Francesca Saffioti ◽  
...  

Author(s):  
Ludmia Taibi ◽  
Anders Boyd ◽  
Nelly Bosselut ◽  
Julie Bottero ◽  
Jérôme Guéchot ◽  
...  

Background Non-invasive methods for assessing liver fibrosis are increasingly used as an alternative to liver biopsy. Recently, a score-based biochemical blood test (Coopscore©) was developed in a cohort of patients chronically infected with hepatitis C virus, showing higher diagnostic performances than Fibrometer®, Fibrotest®, Hepascore® and Fibroscan™. Here, we assess its performance in patients co-infected with the human immunodeficiency virus and hepatitis B virus. Methods Ninety-seven human immunodeficiency virus/hepatitis B virus co-infected patients with liver biopsies were included from a previously described cohort. Histological fibrosis staging using METAVIR criteria was used as the reference. Coopscore©, Fibrotest®, Fibrometer®, Hepascore® and Zeng score were computed and compared with the Coopscore© using the Obuchowski index and area under the receiving operator characteristic curves. Results The distribution of liver fibrosis levels was as follows: F0–F1 ( n = 42), F2 ( n = 25), F3 ( n = 15) and F4 ( n = 15). The Obuchowski index was higher for Coopscore© (0.774) than Fibrometer® (0.668), Hepascore® (0.690) and Zeng scores (0.704) ( P < 0.05), reflecting a better ability to discriminate between fibrosis stages. Similarly, when predicting significant fibrosis (≥F2), the AUROC was significantly greater for the Coopscore© (0.836) than the Hepascore® (0.727) and Zeng scores (0.746), but not for the Fibrotest® (0.778, P = 0.14) or Fibrometer® (0.790, P = 0.19). The Coopscore© did not show a higher capacity than other scores to predict advanced fibrosis (≥F3) or cirrhosis (F4). Conclusions This study supports the diagnostic value of the Coospcore© in fibrosis staging among human immunodeficiency virus/hepatitis B virus co-infected patients, especially to predict significant fibrosis.


2013 ◽  
Vol 12 (6) ◽  
pp. 935-941 ◽  
Author(s):  
Ruediger S. Goertz ◽  
Joerg Sturm ◽  
Lukas Pfeifer ◽  
Dane Wildner ◽  
David L. Wachter ◽  
...  

2019 ◽  
Vol 49 (7) ◽  
pp. 731-742 ◽  
Author(s):  
Koji Fujita ◽  
Kyoko Oura ◽  
Hirohito Yoneyama ◽  
Tingting Shi ◽  
Kei Takuma ◽  
...  

2016 ◽  
Vol 60 (5) ◽  
pp. 587-592 ◽  
Author(s):  
Nicole Harris ◽  
David Nadebaum ◽  
Michael Christie ◽  
Alexandra Gorelik ◽  
Amanda Nicoll ◽  
...  

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Giovanna Ferraioli ◽  
Laura Maiocchi ◽  
Carolina Dellafiore ◽  
Carmine Tinelli ◽  
Elisabetta Above ◽  
...  

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