ArticleJournal of cancer research and clinical oncology2023
Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- 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
- Machine learning-based prognostic model for triple-negative breast cancer with axillary lymph node metastasis.Frontiers in oncology · 2026Article
- DBT-based habitat imaging for differentiating benign and malignant breast architectural distortion : a two-center study.BMC medical imaging · 2025Article
- Review
- Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study.BMC medical imaging · 2025Article
- Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning.Scientific reports · 2025Article
- Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2025Article
- Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer.La Radiologia medica · 2024Article
- Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer.American journal of translational research · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
purposeThis study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma.
methodsThe data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted.
resultsThe clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status.
conclusionThe radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.
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