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Deep learning for staging liver fibrosis on CT: a pilot study

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Abstract

Objectives

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

Methods

This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (FDLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.

Results

The FDLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using FDLCT scores were 0.74 (0.64–0.85), 0.76 (0.66–0.85) and 0.73 (0.62–0.84), respectively.

Conclusions

Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.

Key Points

• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance.

• Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging.

• Further improvement are necessary before utilisation in clinical settings.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

DCNN:

Deep convolutional neural network

DICOM:

Digital Imaging and Communications in Medicine

FDLCT :

Fibrosis score obtained from deep learning based on CT images

IQR:

Interquartile range

JPEG:

Joint Photographic Experts Group

MRE:

Magnetic resonance elastography

ROC:

Receiver operating characteristic

ROI:

Region of interest

TE:

Transient elastography

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

    Koichiro Yasaka, Hiroyuki Akai & Akira Kunimatsu

  2. Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Osamu Abe

  3. Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 537-3 Iguchi, Nasushiobara, Tochigi, 329-2763, Japan

    Shigeru Kiryu

Authors
  1. Koichiro Yasaka
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  2. Hiroyuki Akai
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  3. Akira Kunimatsu
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  4. Osamu Abe
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  5. Shigeru Kiryu
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Corresponding author

Correspondence to Shigeru Kiryu.

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Guarantor

The scientific guarantor of this publication is Koichiro Yasaka.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Yasaka, K., Akai, H., Kunimatsu, A. et al. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol 28, 4578–4585 (2018). https://doi.org/10.1007/s00330-018-5499-7

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  • DOI: https://doi.org/10.1007/s00330-018-5499-7

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