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Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics

European Radiology volume 31, pages 4576–4586 (2021)Cite this article

A Correction to this article was published on 05 February 2021

This article has been updated

Abstract

Objective

To investigate the application of machine learning–based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer.

Methods

Data of 114 consecutive histopathologically confirmed patients with liver cancer from January 2018 to November 2019 were retrospectively analyzed. All patients underwent liver ultrasonography within 1 week before hepatectomy or fine-needle biopsy. The liver lesions were manually segmented by two experts using ITK-SNAP software. Seven categories of radiomics features, including first-order, two-dimensional shape, gray-level co-occurrence matrices, gray-level run-length matrix, gray-level size-zone matrix, neighboring gray tone difference matrix, and gray-level dependence matrix, were extracted on the Pyradiomics platform. Fourteen filters were applied to the original images, and derived images were obtained. Then, the dimensions of radiomics features were reduced by least absolute shrinkage and selection operator (Lasso) method. Finally, k-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) were employed to distinguish primary liver cancer from metastatic liver cancer by a fivefold cross-validation strategy. The performance of the established model was mainly evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy.

Results

One thousand four hundred nine radiomics features were extracted from the original images and/or derived images for each patient. The mentioned five machine learning classifiers were able to differentiate primary liver cancer from metastatic liver cancer. LR outperformed other classifiers, with the accuracy of 0.843 ± 0.078 (AUC, 0.816 ± 0.088; sensitivity, 0.768 ± 0.232; specificity, 0.880 ± 0.117).

Conclusions

Machine learning–based ultrasound radiomics features are able to non-invasively distinguish primary liver tumors from metastatic liver tumors.

Key Points

• Ultrasound-based radiomics was initially used for preoperative classification of primary versus metastatic liver cancer.

• Multiple machine learning–based algorithms with cross-validation strategy were applied to extract machine learning–based ultrasound radiomics features.

• Distinction between primary and metastatic tumors was obtained with a sensitivity of 0.768 and a specificity of 0.880.

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Change history

Abbreviations

CEUS:

Contrast-enhanced ultrasound

DICOM:

Digital Imaging and Communications in Medicine

FADS:

Factor analysis of dynamic structures

FLL:

Focal liver lesion

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size-zone matrix

HCC:

Hepatocellular carcinoma

ICC:

Intrahepatic cholangiocarcinoma

KNN:

k-nearest neighbor

Lasso:

Least absolute shrinkage and selection operator

LDA:

Linear discriminant analysis

LR:

Logistic regression

MLP:

Multilayer perceptron

NGTDM:

Neighboring gray tone difference matrix

PNN:

Probabilistic neural network

RF:

Random forest

SVM:

Support vector machine

TIC:

Time-intensity curve

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Funding

The study was funded by the National Key Research and Development Program of China (Grant No. 2018YFC0114606), National Natural Science Foundation of China (No. 71974065), and Key R & D and Promotion Projects in Henan Province (No. 182400410172).

Author information

Affiliations

  1. Henan Provincial People’s Hospital, Zhengzhou, Henan, China

    Bing Mao, Shaobo Duan & Lianzhong Zhang

  2. Zhengzhou University People’s Hospital, Zhengzhou, Henan, China

    Bing Mao, Shaobo Duan & Lianzhong Zhang

  3. Henan University People’s Hospital, 7 Weiwu Road, Jinshui District, Zhengzhou, 450003, Henan, China

    Bing Mao, Shaobo Duan & Lianzhong Zhang

  4. School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Bing Mao & Jingdong Ma

  5. Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China

    Yuwei Xia

  6. Zhengzhou University, Zhengzhou, Henan, China

    Yaru Tao

Authors
  1. Bing Mao
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  2. Jingdong Ma
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  3. Shaobo Duan
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  4. Yuwei Xia
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Corresponding author

Correspondence to Lianzhong Zhang.

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Guarantor

The scientific guarantor of this publication is Lianzhong Zhang.

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 obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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The original online version of this article was revised: The information was missing that Bing Mao and Jingdong Ma contributed equally to this work.

Bing Mao and Jingdong Ma contributed equally to this work.

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Mao, B., Ma, J., Duan, S. et al. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 31, 4576–4586 (2021). https://doi.org/10.1007/s00330-020-07562-6

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  • DOI: https://doi.org/10.1007/s00330-020-07562-6

Keywords

  • Ultrasonography
  • Machine learning
  • Liver neoplasms
  • Radiomics