Dataset Information

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

ABSTRACT:

Background

Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists.

Methods

A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods.

Results

The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities.

Conclusions

The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

SUBMITTER: Gu Y

PROVIDER: S-EPMC9334487 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

Gu Yang Y Xu Wen W Lin Bin B An Xing X Tian Jiawei J Ran Haitao H Ren Weidong W Chang Cai C Yuan Jianjun J Kang Chunsong C Deng Youbin Y Wang Hui H Luo Baoming B Guo Shenglan S Zhou Qi Q Xue Ensheng E Zhan Weiwei W Zhou Qing Q Li Jie J Zhou Ping P Chen Man M Gu Ying Y Chen Wu W Zhang Yuhong Y Li Jianchu J Cong Longfei L Zhu Lei L Wang Hongyan H Jiang Yuxin Y

Insights into imaging 20220728 1

<h4>Background</h4>Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists.<h4>Methods</h4>A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To ...[more]

PMID: 35900608

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Dataset Information

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

Background

Methods

Results

Conclusions

Publications

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

Similar Datasets

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