ArticleGland surgery2021
Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features.
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 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, 13 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
- Predictors of sentinel lymph node metastasis in cT1-2 cN0 breast cancer: A retrospective cohort study.Medicine · 2026Article
- Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer.Frontiers in oncology · 2025Article
- Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics.Scientific reports · 2024Article
- Current status and future perspectives of contrast-enhanced ultrasound diagnosis of breast lesions.Journal of medical ultrasonics (2001) · 2024Review
- New Insight for Axillary De-Escalation in Breast Cancer Surgery: "SoFT Study" Retrospective Analysis.Current oncology (Toronto, Ont.) · 2024Article
- Predicting three or more metastatic nodes using contrast-enhanced lymphatic US findings in early breast cancer.Insights into imaging · 2024Article
- Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer.Oncology letters · 2024Article
- Advantages of contrast-enhanced ultrasound in the localization and diagnostics of sentinel lymph nodes in breast cancer.Journal of Zhejiang University. Science. B · 2023Article
- A model for the early identification of sentinel lymph node metastasis in patients with breast cancer based on contrast-enhanced ultrasound and clinical features.Oncology letters · 2022Article
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8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSentinel lymph node (SLN) biopsy (SLNB) is the standard procedure for axillary staging in clinically node-negative (cN0) breast cancer patients. However, the positive rate of SLNs among cN0 stage patients is 26-35%. The identification of appropriate candidates for SLNB is quite challenging. This study aimed to establish and verify a predictive model of SLN metastasis using contrast-enhanced ultrasound (CEUS) and other clinicopathological indicators.
methodsThe clinicopathological data of 224 patients who had undergone SLNB at the Affiliated Cancer Hospital of Zhengzhou University from June 2018 to July 2019 were analyzed retrospectively. The risk prediction model of SLN metastasis was established by logistic regression analysis. According to the β value of each variable in the model, a risk score system of SLN metastasis was established and verified using the internal population. The predictive model was prospectively applied to 73 patients from July 2019 to September 2019 to evaluate the clinical value of the model in patients with early breast cancer.
resultsMultivariate analysis confirmed that body mass index (BMI), SLN aspect ratio of CEUS mode, SLN aspect ratio of mammography, lympho-vascular invasion, and cytokeratin (CK)5/6 were independent risk factors for SLN metastasis. A scoring system was established according to the above risk factors, and a receiver operating characteristic (ROC) curve was drawn. After internal- and external verification, a corrected ROC curve was drawn, respectively. The ROC curve of the modeling group, internal verification group, and external verification group was 0.9075 (95% CI: 0.8616-0.9534), 0.8766 (95% CI: 0.8192-0.9341), and 0.8505 (95% CI: 0.7333-0.9676), respectively.
conclusionsWe constructed and verified a prediction model of SLN metastasis in early breast cancer. The model has a specific predictive value for preoperative evaluation of SLN status.
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