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Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?

European Radiology volume 31, pages 244–255 (2021)Cite this article

Abstract

Objective

To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features.

Methods

This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors.

Results

Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI.

Conclusions

Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions.

Key Points

• Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC.

• With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.

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Abbreviations

AUC:

Area under the curve

CC:

Cholangiocarcinoma

cHCC-CC:

Combined hepatocellular cholangiocarcinoma

DWI:

Diffusion-weighted image

HBP:

Hepatobiliary phase

HCC:

Hepatocellular carcinoma

ML:

Machine learning

PCA:

Principal component analysis

RBF:

Radial basis function

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

SD:

Standard deviation

SVM:

Support vector machine

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Funding

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

Author information

Affiliations

  1. Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada

    Xiaoyang Liu & Kartik S. Jhaveri

  2. Lunenfeld Tanenbaum Research Institute, University of Toronto, Toronto, Canada

    Farzad Khalvati & Khashayar Namdar

  3. Department of Pathology, University Health Network, University of Toronto, Toronto, Canada

    Sandra Fischer

  4. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Sara Lewis & Bachir Taouli

  5. Department of Medical Imaging, Lunenfeld Tanenbaum Research Institute and Sinai Health System, University Health Network, University of Toronto, Toronto, Canada

    Masoom A. Haider

Authors
  1. Xiaoyang Liu
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  2. Farzad Khalvati
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  3. Khashayar Namdar
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  4. Sandra Fischer
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  5. Sara Lewis
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  6. Bachir Taouli
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  7. Masoom A. Haider
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  8. Kartik S. Jhaveri
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Correspondence to Kartik S. Jhaveri.

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Dr. Kartik Jhaveri.

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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.

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Written informed consent was waived by the Institutional Review Board.

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  • retrospective

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Cite this article

Liu, X., Khalvati, F., Namdar, K. et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?. Eur Radiol 31, 244–255 (2021). https://doi.org/10.1007/s00330-020-07119-7

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  • DOI: https://doi.org/10.1007/s00330-020-07119-7

Keywords

  • Cholangiocarcinoma
  • Hepatocellular carcinoma
  • Machine learning