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Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making

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

Abstract

Objectives

To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).

Methods and materials

One thousand nineteen breast ultrasound images from 582 patients (age 56.3 ± 11.5 years) were linked to the corresponding radiological report. Lesions were categorized into the following classes: no tissue, normal breast tissue, BI-RADS 2 (cysts, lymph nodes), BI-RADS 3 (non-cystic mass), and BI-RADS 4–5 (suspicious). To test the accuracy of the dCNN, one internal dataset (101 images) and one external test dataset (43 images) were evaluated by the dCNN and two independent readers. Radiological reports, histopathological results, and follow-up examinations served as reference. The performances of the dCNN and the humans were quantified in terms of classification accuracies and receiver operating characteristic (ROC) curves.

Results

In the internal test dataset, the classification accuracy of the dCNN differentiating BI-RADS 2 from BI-RADS 3–5 lesions was 87.1% (external 93.0%) compared with that of human readers with 79.2 ± 1.9% (external 95.3 ± 2.3%). For the classification of BI-RADS 2–3 versus BI-RADS 4–5, the dCNN reached a classification accuracy of 93.1% (external 95.3%), whereas the classification accuracy of humans yielded 91.6 ± 5.4% (external 94.1 ± 1.2%). The AUC on the internal dataset was 83.8 (external 96.7) for the dCNN and 84.6 ± 2.3 (external 90.9 ± 2.9) for the humans.

Conclusion

dCNNs may be used to mimic human decision-making in the evaluation of single US images of breast lesion according to the BI-RADS catalog. The technique reaches high accuracies and may serve for standardization of highly observer-dependent US assessment.

Key Points

• Deep convolutional neural networks could be used to classify US breast lesions.

• The implemented dCNN with its sliding window approach reaches high accuracies in the classification of US breast lesions.

• Deep convolutional neural networks may serve for standardization in US BI-RADS classification.

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Abbreviations

ABUS:

Automatic breast ultrasound

ACR BI-RADS:

American College of Radiology Breast Imaging Reporting and Data System

AUC:

Area under the curve

dCNN:

Deep convolutional neural network

DM:

Digital mammography

MD:

Metadata

NT:

Normal tissue

RIS:

Radiological information system

ROC curves:

Receiver operating characteristic curves

US:

Ultrasound

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Acknowledgements

The work was supported by the Clinical Research Priority Program of the Faculty of Medicine of the University of Zurich “Artificial Intelligence in oncological Imaging Network”.

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

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Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland

    Alexander Ciritsis, Cristina Rossi, Matthias Eberhard, Magda Marcon, Anton S. Becker & Andreas Boss

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  1. Alexander Ciritsis
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  2. Cristina Rossi
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  3. Matthias Eberhard
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  4. Magda Marcon
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  5. Anton S. Becker
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  6. Andreas Boss
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Correspondence to Alexander Ciritsis.

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The scientific guarantor of this publication is Prof. Dr. med Andreas Boss.

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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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No complex statistical methods were necessary for this paper.

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Ciritsis, A., Rossi, C., Eberhard, M. et al. Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. Eur Radiol 29, 5458–5468 (2019). https://doi.org/10.1007/s00330-019-06118-7

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  • DOI: https://doi.org/10.1007/s00330-019-06118-7

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