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

  • Ultrasound
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A Correction to this article was published on 05 February 2021

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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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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

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424

    Article  Google Scholar 

  2. Massarweh NN, El-Serag HB (2017) Epidemiology of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Cancer Control 24(3):1073274817729245

    Article  Google Scholar 

  3. Bosman FT (2010) WHO classification of tumours of the digestive system, 4th edn. International Agency for Research on Cancer, Lyon

    Google Scholar 

  4. Park JH, Kim JH (2019) Pathologic differential diagnosis of metastatic carcinoma in the liver. Clin Mol Hepatol 25(1):12–20

    Article  Google Scholar 

  5. Liu C-Y, Chen K-F, Chen P-J (2015) Treatment of liver cancer. Cold Spring Harb Perspect Med 5(9):a021535

    Article  Google Scholar 

  6. Koehne de Gonzalez AK, Salomao MA, Lagana SM (2015) Current concepts in the immunohistochemical evaluation of liver tumors. World J Hepatol 7(10):1403–1411

    Article  Google Scholar 

  7. Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15(2):81–94

    Article  CAS  Google Scholar 

  8. Sumida Y, Nakajima A, Itoh Y (2014) Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol 20(2):475–485

    Article  Google Scholar 

  9. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  10. Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281(3):947–957

    Article  Google Scholar 

  11. Mao B, Zhang L, Ning P et al (2020) Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol. https://doi.org/10.1007/s00330-020-07056-5

  12. Jin X, Zheng X, Chen D et al (2019) Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol 29(11):6080–6088

    Article  Google Scholar 

  13. Shiraishi J, Sugimoto K, Moriyasu F, Kamiyama N, Doi K (2008) Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography. Med Phys 35(5):1734–1746

    Article  Google Scholar 

  14. Rognin NG, Mercier L, Frinking P et al (2009) Parametric imaging of dynamic vascular patterns of focal liver lesions in contrast-enhanced ultrasound. In: 2009 IEEE International Ultrasonics symposium: Rome, Italy September 20–23, 2009. IEEE. Piscataway, NJ, pp 1282–1285

  15. Wu K, Chen X, Ding M (2014) Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik 125(15):4057–4063

    Article  Google Scholar 

  16. Kondo S, Takagi K, Nishida M et al (2017) Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with perflubutane microbubbles. IEEE Trans Med Imaging 36(7):1427–1437

    Article  Google Scholar 

  17. Gatos I, Tsantis S, Spiliopoulos S et al (2015) A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound. Med Phys 42(7):3948–3959

    Article  Google Scholar 

  18. Gatos I, Tsantis S, Karamesini M et al (2017) Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI. Med Phys 44(7):3695–3705

    Article  Google Scholar 

  19. Gatos I, Tsantis S, Karamesini M, Skouroliakou A, Kagadis G (2015) Development of a support vector machine - based image analysis system for focal liver lesions classification in magnetic resonance images. J Phys Conf Ser 633:12116

    Article  Google Scholar 

  20. Parikh T, Drew SJ, Lee VS et al (2008) Focal liver lesion detection and characterization with diffusion-weighted MR imaging: comparison with standard breath-hold T2-weighted imaging. Radiology 246(3):812–822

    Article  Google Scholar 

  21. Mayerhoefer ME, Schima W, Trattnig S, Pinker K, Berger-Kulemann V, Ba-Ssalamah A (2010) Texture-based classification of focal liver lesions on MRI at 3.0 tesla: a feasibility study in cysts and hemangiomas. J Magn Reson Imaging 32(2):352–359

    Article  Google Scholar 

  22. Liang D, Lin L, Hu H et al (2018) Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images. In: Frangi AF (ed) Medical image computing and computer assisted intervention - MICCAI 2018: 21st International Conference, Granada, Spain, September 16–20, 2018, proceedings / Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger (eds.). Springer, Cham, pp 666–675

  23. Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet 391(10127):1301–1314

    Article  Google Scholar 

  24. Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128

    Article  Google Scholar 

  25. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107

    Article  Google Scholar 

  26. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv:1612.07003

  27. Géron A (2017) Hands-on machine learning with Scikit-learn and TensorFlow: concepts, tools, and techniques to build intelligent systems / Aurélien Géron. O'Reilly, Beijing

    Google Scholar 

  28. Zwanenburg A, Leger S, Agolli L et al (2019) Assessing robustness of radiomic features by image perturbation. Sci Rep 9(1):614

    Article  Google Scholar 

  29. Wu M, Li L, Wang J et al (2018) Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysis. Eur Radiol 28(5):2077–2088

    Article  Google Scholar 

  30. Li W, Huang Y, Zhuang B-W et al (2019) Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol 29(3):1496–1506

    Article  Google Scholar 

  31. Yao Z, Dong Y, Wu G et al (2018) Preoperative diagnosis and prediction of hepatocellular carcinoma: radiomics analysis based on multi-modal ultrasound images. BMC Cancer 18(1):1089

    Article  Google Scholar 

Download references

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

Authors and 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

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