ArticleGland surgery2021
Establishing a prediction model of axillary nodal burden based on the combination of CT and ultrasound findings and the clinicopathological features in patients with early-stage breast cancer.
Article in Gland surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 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
- Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development.JMIR cancer · 2023Article
- Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.Journal of healthcare engineering · 2022Article
- Multiparametric MRI-Derived Habitat Radiomics in Subregional Analysis for Predicting Axillary Lymph Node Metastatic Burden in Breast Cancer.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAxillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB).
methodsThe clinicopathological and imaging data of patients with early invasive breast cancer (cT
resultsHNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance.
conclusionsAs a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB.
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