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Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation.

Methods

This was a retrospective multicenter study with 807 CLD patients and 4842 images from three hospitals. All of these patients have liver biopsy results as referenced standard. Multichannel deep learning radiomics models were developed. Elastography images, gray-scale images of the liver capsule, gray-scale images of the liver parenchyma, and serological results were gradually integrated to establish different diagnosis models, and the optimal model was selected for assessing ≥ F2. Its accuracy was thoroughly investigated by applying different F0–1 prevalence cohorts and independent external test cohorts. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the area under the ROC curve (AUC) for significance of fibrosis (≥ F2) and cirrhosis (F4).

Results

The AUC of the DLRE2.0 model significantly increased to 0.91 compared with the DLRE model (AUC 0.83) when evaluating ≥ F2 (p = 0.0167). However, it did not show statistically significant differences as integrating gray-scale images and serological data into the DLRE2.0 model. AUCs of DLRE and DLRE2.0 increased, when there was higher F0–1 prevalence. All radiomics models had good robustness in the independent external test cohort.

Conclusions

DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability.

Key Points

• The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging.

• We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation.

• Our study based on 807 CLD patients and 4842 images with liver biopsy found that DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability.

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Abbreviations

2D-SWE:

Two-dimensional shear-wave elastography

≥ F2:

Significant fibrosis

≥ F3:

Advanced fibrosis

AI:

Artificial intelligence

ALB:

Albumin

ALT:

Alanine transaminase

APRI:

Aspartate aminotransferase to platelet ratio index

AST:

Aspartate aminotransferase

AUC:

The area under the ROC curve

BMI:

Body mass index

CLD:

Chronic liver diseases

CNN:

Convolutional neural network

CT:

Computed tomography

DB:

Direct bilirubin

DLRE:

Deep learning radiomics model of elastography

F4:

Cirrhosis

FBG:

Fasting blood glucose

FIB-4:

Fibrosis index based on four factors

GGT:

Gamma-glutamyl transpeptidase

IB:

Indirect bilirubin

LB:

Liver biopsy

MLP:

Multi-layer perceptron

MR:

Magnetic resonance

PLT:

Platelet count

PT%:

Prothrombin activity percentage

ROC:

Receiver operating characteristic curves

TB:

Total bilirubin

TE:

Transient elastography

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Acknowledgements

We thank all the patients involved in this study. The authors would like to acknowledge the instrumental and technical support of multimodal biomedical imaging experimental platform, Institute of Automation, Chinese Academy of Sciences.

Funding

The work is supported by the National Natural Science Foundation of China under Grant No. 81827802, 81527805, 61231004, 61671449, and 61401462, the Chinese Academy of Sciences under Grant No. KFJ-STS-ZDTP-059, YJKYYQ20180048, QYZDJ-SSW-JSC005, and XDB32030200.

Author information

Authors and Affiliations

  1. Department of Ultrasound, Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, 510630, China

    Xue Lu, Jieyang Jin & Rongqin Zheng

  2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China

    Hui Zhou, Kun Wang & Jie Tian

  3. University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing, People’s Republic of China, 100049

    Hui Zhou, Kun Wang & Jie Tian

  4. Department of Ultrasound, Lingnan Hospital of the Third Affiliated Hospital of Sun Yat-sen University, 2693 Kaichuang Road, Guangzhou, 510700, China

    Jieyang Jin

  5. Function Diagnosis Center, Beijing Youan Hospital affiliated to Capital Medical University, Beijing, 100069, China

    Fankun Meng & Xiaojie Mu

  6. Faculty of Engineering and Information Sciences (EIS), University of Wollongong, Wollongong, Australia

    Shuoyang Li

  7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, 95 Zhongguancun East Road, Beijing, 100191, China

    Jie Tian

Authors
  1. Xue Lu
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Corresponding authors

Correspondence to Rongqin Zheng or Jie Tian.

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Guarantor

The scientific guarantor of this publication is Prof. Rongqin Zheng

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

One of the authors has significant statistical expertise.

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Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Lu, X., Zhou, H., Wang, K. et al. Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease. Eur Radiol 31, 8743–8754 (2021). https://doi.org/10.1007/s00330-021-07934-6

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  • DOI: https://doi.org/10.1007/s00330-021-07934-6

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