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A New Model Incorporating Axillary Ultrasound After Neoadjuvant Chemotherapy to Predict Non-Sentinel Lymph Node Metastasis in Invasive Breast Cancer

PURPOSE: Few models with good discriminative power have been introduced to predict the risk of non-sentinel lymph node (non-SLN) metastasis in breast cancer after neoadjuvant chemotherapy (NAC). We aimed to develop a new and simple model for predicting the probability of non-SLN metastasis in breast...

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Detalles Bibliográficos
Autores principales: Zhang, Kai, Zhu, Qian, Sheng, Danli, Li, Jiawei, Chang, Cai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020912/
https://www.ncbi.nlm.nih.gov/pubmed/32104078
http://dx.doi.org/10.2147/CMAR.S239921
Descripción
Sumario:PURPOSE: Few models with good discriminative power have been introduced to predict the risk of non-sentinel lymph node (non-SLN) metastasis in breast cancer after neoadjuvant chemotherapy (NAC). We aimed to develop a new and simple model for predicting the probability of non-SLN metastasis in breast cancer and facilitate the selection of patients who could avoid unnecessary axillary lymph node dissection following NAC. PATIENTS AND METHODS: A total of 298 patients diagnosed with invasive breast cancer, who underwent SLN biopsy after completing NAC and subsequently breast surgery, were included and classified into the training set (n=228) and testing set (n=70). Univariate and multivariate analyses were used to select factors that could be determined prior to breast surgery and significantly correlated with non-SLN metastasis in the training set. A logistic regression model was developed based on these factors and validated in the testing set. RESULTS: Nodal status before NAC, post-NAC axillary ultrasound status, SLN number, and SLN metastasis number were independent predictors of non-SLN metastases in breast cancer after NAC. A predictive model based on these factors yielded an area under the curve of 0.838 (95% confidence interval: 0.774–0.902, p< 0.001) in the training set. When applied to the testing set, this model yielded an area under the curve of 0.808 (95% confidence interval: 0.609–1.000, p= 0.003). CONCLUSION: A new and simple model, which incorporated factors that could be determined prior to breast surgery, was developed to predict non-SLN metastasis in invasive breast cancer following NAC. Although this model performed excellently in internal validation, it requires external validation before it can be widely utilized in the clinical setting.