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Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics

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Abstract

The purpose of this study is to test the feasibility for deep CNN–based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature–based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN–based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN–based AI methods, while radiomic feature–based machine learning methods achieved inferior classification results.

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Funding

This work was mainly supported by the National Natural Science Foundation of China (grant number 81671743), the Clinical Key Diseases Diagnosis and Therapy Special Project of Health and Family Planning Commission of Suzhou (LCZX201801), the Program for Advanced Talents within Six Industries of Jiangsu province (WSW-057), the High-level Health Personnel “six-one” Project of Jiangsu province in China (LGY2016035), the Livelihood Technology Project of Health and Family Planning Commission of Suzhou (SYSD2020074), and Applied Basic Research Project in Suzhou (SYS2018083).

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Author notes

  1. Longxiu Qi, Xing Lu, and Hailin Shen contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu Province, Jiangsu Province, 215006, Suzhou City, China

    Longxiu Qi & Yonggang Li

  2. Department of Radiology, The First People’s Hospital of Yancheng, Yancheng City, Jiangsu Province, China

    Longxiu Qi

  3. Sanmed Biotech Inc, Zhuhai City, Guangzhou Province, China

    Xing Lu

  4. Department of Radiology, Suzhou Kowloon Hospital, Shanghai Jiaotong University School of Medicine, Jiangsu Province, 215028, Suzhou City, China

    Hailin Shen

  5. Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China

    Qilei Gao

  6. Department of Radiology, Huaian Maternal and Child Health Hospital, Huaian City, Jiangsu Province, China

    Zhigang Han

  7. Department of Radiology, Xinghai Hospital of Suzhou Industry Park, Suzhou City, Jiangsu Province, China

    Jianguo Zhu

  8. Department of Breast Surgery, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China

    You Meng

  9. Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, China

    Linhua Wang

  10. Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Jiangsu Province, 215001, Suzhou City, China

    Shuangqing Chen

Authors
  1. Longxiu Qi
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  2. Xing Lu
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  3. Hailin Shen
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  4. Qilei Gao
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  5. Zhigang Han
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  6. Jianguo Zhu
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  7. You Meng
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  8. Linhua Wang
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  9. Shuangqing Chen
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  10. Yonggang Li
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Contributions

All authors contributed to the study conception and design. Longxiu Qi, Hailin Shen, and Qilei Gao: investigation, data curation, visualization, writing original draft. Zhigang Han, Jianguo Zhu, and You Meng: data curation and data analysis. Xing Lu: software development, data analysis, and editing. Yonggang Li, Linhua Wang, and Shuangqing Chen: investigation, supervision, review, and editing. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Linhua Wang, Shuangqing Chen or Yonggang Li.

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Ethics Approval and Consent to Participate

This multicenter study was approved by the ethics committee of every participating hospital and was conducted in accordance with the Declaration of Helsinki. The requirement for patient informed consent was waived.

Competing Interests

The authors declare no competing interests.

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Qi, L., Lu, X., Shen, H. et al. Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics. J Digit Imaging 36, 1314–1322 (2023). https://doi.org/10.1007/s10278-023-00798-w

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  • DOI: https://doi.org/10.1007/s10278-023-00798-w

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