Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liveropen access
- Authors
- Choi, Kyu Jin; Jang, Jong Keon; Lee, Seung Soo; Sung, Yu Sub; Shim, Woo Hyun; Kim, Ho Sung; Yun, Jessica; Choi, Jin-Young; Lee, Yedaun; Kang, Bo kyeong; Kim, Jin Hee; Kim, So Yeon; Yu, Eun Sil
- Issue Date
- Dec-2018
- Publisher
- RADIOLOGICAL SOC NORTH AMERICA
- Citation
- RADIOLOGY, v.289, no.3, pp.688 - 697
- Indexed
- SCIE
SCOPUS
- Journal Title
- RADIOLOGY
- Volume
- 289
- Number
- 3
- Start Page
- 688
- End Page
- 697
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148840
- DOI
- 10.1148/radiol.2018180763
- ISSN
- 0033-8419
- Abstract
- Purpose
To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver.
Materials and Methods
DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist’s assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index.
Results
In the test data sets, the DLS had a staging accuracy of 79.4% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2–4), advanced fibrosis (F3–4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist’s interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71–0.81; P ˂ .001) for staging liver fibrosis.
Conclusion
The deep learning system allows for accurate staging of liver fibrosis by using CT images.
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