ArticleEuropean radiology2022
Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 40 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
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- Intra- and peritumoral radiomics for predicting equivocal HER2 status of breast cancer on contrast-enhanced mammography.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
- MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.International journal of general medicine · 2026Article
- A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- MRI radiomics for diagnosing small BI-RADS 4 breast lesions: an interpretable model.Quantitative imaging in medicine and surgery · 2025Article
- Rapid sorting and auxiliary evaluation of malignant breast tumors by accurate imaging analysis of metastasis-related biomarker.Science advances · 2025Article
- Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study.Insights into imaging · 2025Article
- Early Detection of Breast Cancer in MRI Using AI.Academic radiology · 2025Article
- Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.BMC cancer · 2025Article
- Predicting Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer Based on Radiomics of MRI Habitat and US.Breast cancer (Dove Medical Press) · 2025Article
- Mammography Based Nomogram Integrating Radiomics and Clinical Features to Predict Benign or Malignant Regression of BI-RADS 4A Lesions at Follow-Up.International journal of women's health · 2025Article
- Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Whole tumour- and subregion-based radiomics of contrast-enhanced mammography in differentiating HER2 expression status of invasive breast cancers: A double-centre pilot study.British journal of cancer · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- State-of-the-art for contrast-enhanced mammography.The British journal of radiology · 2024Review
- Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.Frontiers in immunology · 2024Article
- Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.British journal of cancer · 2023Article
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10 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo conduct perilesional region radiomics analysis of contrast-enhanced mammography (CEM) images to differentiate benign and malignant breast lesions. METHODS AND MATERIALS: This retrospective study included patients who underwent CEM from November 2017 to February 2020. Lesion contours were manually delineated. Perilesional regions were automatically obtained. Seven regions of interest (ROIs) were obtained for each lesion, including the lesion ROI, annular perilesional ROIs (1 mm, 3 mm, 5 mm), and lesion + perilesional ROIs (1 mm, 3 mm, 5 mm). Overall, 4,098 radiomics features were extracted from each ROI. Datasets were divided into training and testing sets (1:1). Seven classification models using features from the seven ROIs were constructed using LASSO regression. Model performance was assessed by the AUC with 95% CI.
resultsOverall, 190 women with 223 breast lesions (101 benign; 122 malignant) were enrolled. In the testing set, the annular perilesional ROI of 3-mm model showed the highest AUC of 0.930 (95% CI: 0.882-0.977), followed by the annular perilesional ROI of 1 mm model (AUC = 0.929; 95% CI: 0.881-0.978) and the lesion ROI model (AUC = 0.909; 95% CI: 0.857-0.961). A new model was generated by combining the predicted probabilities of the lesion ROI and annular perilesional ROI of 3-mm models, which achieved a higher AUC in the testing set (AUC = 0.940).
conclusionsAnnular perilesional radiomics analysis of CEM images is useful for diagnosing breast cancers. Adding annular perilesional information to the radiomics model built on the lesion information may improve the diagnostic performance. KEY POINTS: • Radiomics analysis of the annular perilesional region of 3 mm in CEM images may provide valuable information for the differential diagnosis of benign and malignant breast lesions. • The radiomics information from the lesion region and the annular perilesional region may be complementary. Combining the predicted probabilities of the models constructed by the features from the two regions may improve the diagnostic performance of radiomics models.
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