ArticleQuantitative imaging in medicine and surgery2021
Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 11 citations in OpenAlex.
- Improving the malignancy prediction of breast cancer based on the integration of radiomics features from dual-view mammography and clinical parameters.Clinical and experimental medicine · 2023Trial
- Impact of synthetic data on training a deep learning model for lesion detection and classification in contrast-enhanced mammography.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- 조영증강 유방촬영술: 현재의 임상적 활용과 미래 방향에 대한 종합적 고찰.Journal of the Korean Society of Radiology · 2025Review
- State-of-the-art for contrast-enhanced mammography.The British journal of radiology · 2024Review
- Development of a nomogram-based model combining intra- and peritumoral ultrasound radiomics with clinical features for differentiating benign from malignant in Breast Imaging Reporting and Data System category 3-5 nodules.Quantitative imaging in medicine and surgery · 2023Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.Frontiers in radiology · 2023Review
- Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions.Methods and protocols · 2022Review
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Authors and funding
10 authors at 6 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundContrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions.
methodsThis retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlation and Boruta's approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests.
resultsA total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918-1.000) for the combined model, 0.947 (95% CI: 0.891-0.997) for the radiomics model, and 0.882 (95% CI: 0.803-0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050).
conclusionsIncorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions.
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