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.
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.
Authors: Jai Min Ryu; Se Kyung Lee; Ji Young Kim; Jonghan Yu; Seok Won Kim; Jeong Eon Lee; Se Hwan Han; Yong Sik Jung; Seok Jin Nam Journal: Clin Breast Cancer Date: 2017-04-06 Impact factor: 3.225
Authors: David N Krag; Stewart J Anderson; Thomas B Julian; Ann M Brown; Seth P Harlow; Takamaru Ashikaga; Donald L Weaver; Barbara J Miller; Lynne M Jalovec; Thomas G Frazier; R Dirk Noyes; André Robidoux; Hugh M C Scarth; Denise M Mammolito; David R McCready; Eleftherios P Mamounas; Joseph P Costantino; Norman Wolmark Journal: Lancet Oncol Date: 2007-10 Impact factor: 41.316
Authors: Armando E Giuliano; Karla V Ballman; Linda McCall; Peter D Beitsch; Meghan B Brennan; Pond R Kelemen; David W Ollila; Nora M Hansen; Pat W Whitworth; Peter W Blumencranz; A Marilyn Leitch; Sukamal Saha; Kelly K Hunt; Monica Morrow Journal: JAMA Date: 2017-09-12 Impact factor: 56.272
Authors: Judy C Boughey; Karla V Ballman; Huong T Le-Petross; Linda M McCall; Elizabeth A Mittendorf; Gretchen M Ahrendt; Lee G Wilke; Bret Taback; Eric C Feliberti; Kelly K Hunt Journal: Ann Surg Date: 2016-04 Impact factor: 12.969