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Purpose <p>This study aims to explore the relationship between ultrasound radiomics features and semantic features from BI-RADS classification in the preoperative differentiation of benign and malignant breast lesions, as well as the potential diagnostic advantages of radiomics features.</p> Methods <p>Retrospective analysis was performed on 147 female patients with pathologically confirmed breast lesions. Ultrasound images and clinical data were used to construct three diagnostic models: BI-RADS classification single factor diagnostic model, Radiomics diagnostic model, and a BI-RADS-radiomic combined model. Additionally, univariate radiomic models based on semantic features were developed to investigate the associations.</p> Results <p>The BI-RADS-Radiomics combined model demonstrated superior performance in both training and testing sets, with AUC values of 0.985 and 0.964, respectively. It also exhibited optimal diagnostic consistency and clinical net benefit. Significant correlations were observed between multiple radiomics features and specific semantic features (AUC range: 0.609–0.752).</p> Conclusion <p>Radiomics features effectively assist in breast cancer diagnosis via ultrasound and exhibit nonlinear associations with specific semantic features.</p>

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Diagnostic value of ultrasound radiomic features in differentiating benign and malignant breast lesions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Yuke Gong,
  • Yan Cheng,
  • Yan Liu,
  • Guohui Zhang,
  • Shuang Li,
  • Ruiqi Wu,
  • Hongmei Wang,
  • Lizhou Lu

摘要

Purpose

This study aims to explore the relationship between ultrasound radiomics features and semantic features from BI-RADS classification in the preoperative differentiation of benign and malignant breast lesions, as well as the potential diagnostic advantages of radiomics features.

Methods

Retrospective analysis was performed on 147 female patients with pathologically confirmed breast lesions. Ultrasound images and clinical data were used to construct three diagnostic models: BI-RADS classification single factor diagnostic model, Radiomics diagnostic model, and a BI-RADS-radiomic combined model. Additionally, univariate radiomic models based on semantic features were developed to investigate the associations.

Results

The BI-RADS-Radiomics combined model demonstrated superior performance in both training and testing sets, with AUC values of 0.985 and 0.964, respectively. It also exhibited optimal diagnostic consistency and clinical net benefit. Significant correlations were observed between multiple radiomics features and specific semantic features (AUC range: 0.609–0.752).

Conclusion

Radiomics features effectively assist in breast cancer diagnosis via ultrasound and exhibit nonlinear associations with specific semantic features.