ArticleJournal of ultrasound2025
Diagnostic value of ultrasound radiomic features in differentiating benign and malignant breast lesions.
Article in Journal of ultrasound, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Parsimonious Ultrasound Radiomics and Ki-67 Model for Estimating MammaPrint Risk Categorization in HR+/HER2- Early Breast Cancer.Current oncology (Toronto, Ont.) · 2026Article
- A Systematic Review and Meta-analysis of Two-Dimensional Breast Ultrasound Radiomics: Implications for Lesion Characterization and Diagnostic Accuracy.Journal of imaging informatics in medicine · 2026Review
- Radiomics analysis of biplanar ultrasound images can discriminate non-mass breast carcinoma from mastitis.Frontiers in oncology · 2026Article
- A Multimodal Ultrasound Radiomics Study for Discriminating Benign and Malignant Breast Lesions Classified as BI-RADS Category 4.International journal of general medicine · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis 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.
methodsRetrospective 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.
resultsThe 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).
conclusionRadiomics features effectively assist in breast cancer diagnosis via ultrasound and exhibit nonlinear associations with specific semantic features.
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Registered trials
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