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A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network

Abstract

Objectives

To develop a dual-modal neural network model to characterize ultrasound (US) images of breast masses.

Materials and methods

A combined US B-mode and color Doppler neural network model was developed to classify US images of the breast. Three datasets with breast masses were originally detected and interpreted by 20 experienced radiologists according to Breast Imaging-Reporting and Data System (BI-RADS) lexicon ((1) training set, 103212 masses from 45,433 + 12,519 patients. (2) held-out validation set, 2748 masses from 1197 + 395 patients. (3) test set, 605 masses from 337 + 78 patients). The neural network was first trained on training set. Then, the trained model was tested on a held-out validation set to evaluate agreement on BI-RADS category between the model and the radiologists. In addition, the model and a reader study of 10 radiologists were applied to the test set with biopsy-proven results. To evaluate the performance of the model in benign or malignant classifications, the receiver operating characteristic curve, sensitivities, and specificities were compared.

Results

The trained dual-modal model showed favorable agreement with the assessment performed by the radiologists (κ = 0.73; 95% confidence interval, 0.71–0.75) in classifying breast masses into four BI-RADS categories in the validation set. For the binary categorization of benign or malignant breast masses in the test set, the dual-modal model achieved the area under the ROC curve (AUC) of 0.982, while the readers scored an AUC of 0.948 in terms of the ROC convex hull.

Conclusion

The dual-modal model can be used to assess breast masses at a level comparable to that of an experienced radiologist.

Key Points

• A neural network model based on ultrasonic imaging can classify breast masses into different Breast Imaging-Reporting and Data System categories according to the probability of malignancy.

• A combined ultrasonic B-mode and color Doppler neural network model achieved a high level of agreement with the readings of an experienced radiologist and has the potential to automate the routine characterization of breast masses.

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Abbreviations

AUC:

Area under the ROC curve

BI-RADS:

Breast Imaging-Reporting and Data System

CI:

Confidence interval

CNN:

Convolutional neural network

ROC:

Receiver operating characteristic curve

US:

Ultrasound

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Funding

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

Author information

Correspondence to Zeyu Chen.

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Guarantor

The scientific guarantor of this publication is Zeyu Chen.

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Multicenter study

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Cite this article

Qian, X., Zhang, B., Liu, S. et al. A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network. Eur Radiol (2020). https://doi.org/10.1007/s00330-019-06610-0

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Keywords

  • B-mode ultrasound
  • Color Doppler
  • Breast mass
  • Neural network
  • Breast Imaging-Reporting and Data System categories