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European Radiology
Published in: European Radiology 5/2020

01-05-2020 | Ultrasound | Imaging Informatics and Artificial Intelligence

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

Authors: Xuejun Qian, Bo Zhang, Shaoqiang Liu, Yueai Wang, Xiaoqiong Chen, Jingyuan Liu, Yuzheng Yang, Xiang Chen, Yi Wei, Qisen Xiao, Jie Ma, K. Kirk Shung, Qifa Zhou, Lifang Liu, Zeyu Chen

Published in: European Radiology | Issue 5/2020

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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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Metadata
Title
A combined ultrasonic B-mode and color Doppler system for the classification of breast masses using neural network
Authors
Xuejun Qian
Bo Zhang
Shaoqiang Liu
Yueai Wang
Xiaoqiong Chen
Jingyuan Liu
Yuzheng Yang
Xiang Chen
Yi Wei
Qisen Xiao
Jie Ma
K. Kirk Shung
Qifa Zhou
Lifang Liu
Zeyu Chen
Publication date
01-05-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 5/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06610-0

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