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Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver

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

Abstract

Objectives

To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI.

Methods

This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (n = 72), resections (n = 29), liver transplants (n = 46), and autopsies (n = 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System [LI-RADS] criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN.

Results

The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN’s performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed.

Conclusion

This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as “ground truth.”

Key Points

• A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features.

• The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3 ms which paves the way for clinical application as a decision support instrument.

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Abbreviations

AUC:

Area under the curve

CNN:

Convolutional neural network

FNH:

Focal nodular hyperplasia

HCC:

Hepatocellular carcinoma

HIPAA:

Health Insurance Portability and Accountability Act

ICC:

Intrahepatic cholangiocarcinoma

LI-RADS:

Liver Imaging Reporting and Data System

MELD:

Model for End-Stage Liver Disease

NPV:

Negative predictive value

PACS:

Picture archiving and communication system

PPV:

Positive predictive value

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Funding

CW received funding from the Radiological Society of North America (RSNA Research Resident Grant #RR1731). JD, JC, ML, and CW received funding from the National Institutes of Health (NIH/NCI R01 CA206180).

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Authors and Affiliations

  1. Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA

    Paula M. Oestmann, Clinton J. Wang, Lynn J. Savic, Charlie A. Hamm, Sophie Stark, Isabel Schobert, Todd Schlachter, MingDe Lin, Jeffrey C. Weinreb, James S. Duncan & Julius Chapiro

  2. Institute of Radiology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, 10117, Berlin, Germany

    Paula M. Oestmann, Lynn J. Savic, Charlie A. Hamm, Sophie Stark, Isabel Schobert & Bernhard Gebauer

  3. Faculty of Medicine, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

    Paula M. Oestmann

  4. Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, 06520, USA

    Clinton J. Wang & James S. Duncan

  5. Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg, Germany

    Sophie Stark

  6. Department of Transplantation and Immunology, 333 Cedar Street, New Haven, CT, 06520, USA

    Ramesh Batra & David Mulligan

  7. Department of Pathology, Yale School of Medicine, 310 Cedar Street, New Haven, CT, 06520, USA

    Xuchen Zhang

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  1. Paula M. Oestmann
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  15. Julius Chapiro
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Corresponding author

Correspondence to Julius Chapiro.

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Guarantor

The scientific guarantor of this publication is Dr. Julius Chapiro, MD, PhD.

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.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic study

• performed at one institution

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Oestmann, P.M., Wang, C.J., Savic, L.J. et al. Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver. Eur Radiol 31, 4981–4990 (2021). https://doi.org/10.1007/s00330-020-07559-1

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  • DOI: https://doi.org/10.1007/s00330-020-07559-1

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