ArticleThe British journal of radiology2023
Radiomic analysis in contrast-enhanced mammography using a multivendor data set: accuracy of models according to segmentation techniques.
Article in The British journal of radiology, 2023. 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.
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- 조영증강 유방촬영술: 현재의 임상적 활용과 미래 방향에 대한 종합적 고찰.Journal of the Korean Society of Radiology · 2025Review
- Contrast-Enhanced Mammography in Breast Lesion Assessment: Accuracy and Surgical Impact.Tomography (Ann Arbor, Mich.) · 2025Article
- Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.Frontiers in radiology · 2023Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRadiomic analysis of contrast-enhanced mammographic (CEM) images is an emerging field. The aims of this study were to build classification models to distinguish benign and malignant lesions using a multivendor data set and compare segmentation techniques.
methodsCEM images were acquired using Hologic and GE equipment. Textural features were extracted using MaZda analysis software. Lesions were segmented with freehand region of interest (ROI) and ellipsoid_ROI. Benign/Malignant classification models were built using extracted textural features. Subset analysis according to ROI and mammographic view was performed.
results269 enhancing mass lesions (238 patients) were included. Oversampling mitigated benign/malignant imbalance. Diagnostic accuracy of all models was high (>0.9). Segmentation with ellipsoid_ROI produced a more accurate model than with FH_ROI, accuracy:0.947
conclusionsAccurate radiomics models can be built using a real-life multivendor data set segmentation with ellipsoid-ROI produces the highest level of accuracy. The marginal increase in accuracy using both mammographic views, may not justify the increased workload. ADVANCES IN KNOWLEDGE: Radiomic modelling can be successfully applied to a multivendor CEM data set, ellipsoid_ROI is an accurate segmentation technique and it may be unnecessary to segment both CEM views. These results will help further developments aimed at producing a widely accessible radiomics model for clinical use.
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