ArticleCancer management and research2020
Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer.
Article in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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21 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- 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
- Predicting axillary lymph node metastasis in breast cancer via quantitative analysis of VueBox ultrasound contrast-enhanced imaging combined with multimodal feature fusion.Quantitative imaging in medicine and surgery · 2026Article
- Development and validation of a novel nomogram predicting axillary lymph node metastasis among breast cancer patients in Egypt.Scientific reports · 2026Article
- Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters.Breast cancer research : BCR · 2025Article
- Predictors of Non-Sentinel Nodal Involvement in Breast Cancer Patients with Positive Sentinel Nodes Undergoing Upfront Surgery.Pakistan journal of medical sciences · 2025Article
- Sonographic localisation of lymph nodes suspicious of metastatic breast cancer to surgical axillary levels.Journal of medical radiation sciences · 2025Review
- A preoperative prediction model for ipsilateral axillary lymph node metastasis of breast cancer based on clinicopathological and ultrasonography features: a prospective cohort study.Frontiers in oncology · 2025Article
- Preoperative prediction of HER2 expression and sentinel lymph node status in breast cancer using a mammography radiomics model.Frontiers in oncology · 2025Article
- Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.BMC women's health · 2024Article
- Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.Molecular imaging and biology · 2024Article
- Predictive factors of axillary lymph node involvement in Tunisian women with early breast cancer.African health sciences · 2023Article
- Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development.JMIR cancer · 2023Article
- Article
- Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms: a prospective single-center observational study.European radiology · 2022Observational
- Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases.BMC cancer · 2022Article
- A preoperative nomogram for predicting the risk of sentinel lymph node metastasis in patients with T1-2N0 breast cancer.Japanese journal of radiology · 2022Article
- Article
- Clinical Value of Preoperative Ultrasound Signs in Evaluating Axillary Lymph Node Status in Triple-Negative Breast Cancer.Journal of oncology · 2022Article
- A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.Frontiers in oncology · 2022Article
- Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients.Frontiers in oncology · 2020Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAxillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement. PATIENTS AND
methodsA total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate.
resultsThe ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate.
conclusionThe successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.
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