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An effective fine grading method of BI-RADS classification in mammography

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Mammography is an important imaging technique for the detection of early breast cancer. Doctors classify mammograms as Breast Imaging Reporting and Data Systems (BI-RADS). This study aims to provide an intelligent BI-RADS grading prediction method, which can help radiologists and clinicians to distinguish the most challenging 4A, 4B, and 4C cases in mammography.

Methods

Firstly, the breast region, the lesion region, and the corresponding region in the contralateral breast were extracted. Four categories of features were extracted from the original images and the images after the wavelet transform. Secondly, an optimized sequential forward floating selection (SFFS) was used for feature selection. Finally, a two-layer classifier integration was employed for fine grading prediction. 45 cases from the hospital and 500 cases from Digital Database for Screening Mammography (DDSM) database were used for evaluation.

Results

The classification performance of the support vector machine (SVM), Bayes, and random forest is very close on the 45 testing set, with the area under the receiver operating characteristic curve (AUC) of 0.978, 0.967, and 0.968. On the DDSM set, the AUC achieves 0.931, 0.938, and 0.874. Using the mean probability prediction, the AUC on the two datasets reaches 0.998 and 0.916. However, they are all significantly higher than the doctors’ diagnosis, with the AUC of 0.807 and 0.725.

Conclusions

A BI-RADS fine grading (2, 3, 4A, 4B, 4C, 5) prediction model was proposed. Through the evaluation from different datasets, the performance is proved higher than that of the doctors, which may provide great help for clinical BI-RADS classification diagnosis. Therefore, our method can produce more effective and reliable results.

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Acknowledgements

This work was supported by the Guizhou Province Science and Technology Project under Grant Qiankehezhicheng [2019] 2794, the Fundamental Research Funds for the Central Universities under Grant N2119003, 2017 young and middle-aged scientific and technological innovation talent support plan under Grant [RC170497], Guiyang Science and Technology Plan under Grant [2017] 30-34, National Natural Science Foundation of China (NSFC) under Grant 61701103, and Natural Science Foundation of Liaoning Province under Grant 2019-ZD-0005.

Funding

This work was supported by the Guizhou Province Science and Technology Project under Grant Qiankehezhicheng [2019] 2794, the Fundamental Research Funds for the Central Universities under Grant N2119003, 2017 young and middle-aged scientific and technological innovation talent support plan under Grant [RC170497], Guiyang Science and Technology Plan under Grant [2017] 30-34, National Natural Science Foundation of China (NSFC) under Grant 61701103, and Natural Science Foundation of Liaoning Province under Grant 2019-ZD-0005.

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Author notes

  1. Fei Lin and Hang Sun contributed equally to this work.

Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Fei Lin, Hang Sun, Nan Bao & Hong Li

  2. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China

    Lu Han & Tao Yu

  3. Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China

    Jing Li, Jing Chen & Shi Zhou

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  1. Fei Lin
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All authors contributed to the study conception and design. All authors read and approved the final manuscript.

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Correspondence to Hong Li or Tao Yu.

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Lin, F., Sun, H., Han, L. et al. An effective fine grading method of BI-RADS classification in mammography. Int J CARS 17, 239–247 (2022). https://doi.org/10.1007/s11548-021-02541-8

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