Nomogram For Predicting The Likelihood Of Additional Nodal Metastases In Breast Cancer Patients With A Positive Sentinel Node Biopsy Of Chinese Female Breast Cancer Population

Part One Establishment and Internal Validation of Non-sentinel LymphNode Metastasis Model in Chinese Female Breast CancerObjective: Breast cancer patients with sentinel lymph node(SLN)metastases may have a low risk of non-SLN metastases.Accurate estimates of the likelihood of additional disease in the non-SLN metastases can avoid many complications mentioned the axillary lymph node dissection(ALND).This study aims to develop a new model based on Chinese real-world patients to ascertain the likelihood of non-SLN metastases in breast cancer patients with disease-positive SLN,enabling the surgeons to make a better choice of surgical procedures.Methods: Out of the 470 patients from CSCO Breast Cancer Database collaborated Group,a proportion of 3(347 cases): 1(123 cases)was considered for assigning patients to training and validation groups,respectively.Two training models were created to predict the likelihood of having additional,non-SLN metastases in an individual patient.Training model was created with pathological size of the tumor,pathological type,lymphovascular invasion,the number of positive SLNs/number of total SLNs ratio,and the Her-2 status based on multivariable logistic regression(P <0.05).Result: The area under the receiver operating characteristic(ROC)curve of the training model was 0.754,the validation cohort(n=123)was developed to confirm the model 1's performance and the ROC curve was 0.703.The nomogram achieved good concordance indexes of 0.754(95% CI,0.702-0.807)and 0.703(95% CI,0.609-0.796)in predicting the non-SLN metastases in the training and validation cohorts,respectively,with well-fitted calibrationcurves.The positive and negative predictive values of the nomogram were calculated,resulting in positive values of 59.3% and 48.6% and negative predictive values of 79.7% and 83.0% for the training and validation cohorts,respectively.Conclusion: The model established by the validation group has good predictive power.The model variables are used to validate the validation group.The area under the ROC curve is 0.703,indicating that the model created with pathological size of the tumor,pathological type,lymphovascular invasion,the number of positive SLNs/number of total SLNs ratio,and the Her-2 status has good clinical prediction performance.Part Two The Comparison of models of non-sentinel axillary lymph node metastasis in breast cancerObjective: At present,there are many models for predicting non-sentinel lymph node metastasis,but these models are not effective in predicting non-sentinel axillary lymph node metastasis in Chinese women with breast cancer.Therefore,we need to select a non-sentinel lymph node metastasis prediction model that is more suitable for Chinese women with breast cancer and predict non-sentinel lymph node metastasis.Methods: Out of the 470 patients from CSCO Breast Cancer Database collaborated Group,a proportion of 3(347 cases): 1(123 cases)was considered for assigning patients to training and validation groups,respectively.Training model A was created with pathological size of the tumor,pathological type,lymphovascular invasion,the number of positive SLNs/number of total SLNs ratio,and the Her-2 status based on multivariable logistic regression(P<0.05).Training model B was created with pathological size of the tumor and the number of positive SLNs/number of total SLNs ratio,(P<0.001).Training model C was based on all the variables.Data were then used to calculate the probability of further metastases in NSLNs predicted by the 2 nomograms that are currently in use: the MSKCC(Memorial Sloan Kettering Cancer Center),MDACC(University of Texas MD AndersonCancer Center),models.The area under the receiver operating characteristic(ROC)curve(AUC)was 0.695 and 0.630 calculated for each nomogram.Result: The area under the receiver operating characteristic(ROC)curve of the training model A was 0.754,The AUC of model B was 0.681.The area of training model C was 0.766.AUC values for the MSKCC,MDACC models are 0.63,and 0.74,respectively.There was no difference between model A and model C regarding the ROC curve,P=0.0558.There was significant difference between model A and model B regarding the ROC curve,P=0.0027.Conclusion: We developed five models that used information commonly available to the surgeon to calculate the likelihood of having non-SLN metastases in an individual patient.The numbers of variables in model A were less than in model C,while model A had similar results as model C in calculating the likelihood of having non-SLN metastases in an individual patient.The area under MSKCC and MDACC models is smaller than model A.Model A was more user-friendly nomogram than model C.Using model A,the risk for an individual patient having axillary lymph node dissection(ALND)could be determined,which would lead to a rational therapeutic choice.Part Three Non-sentinel lymph node metastasis prediction model validation outside of Chinese female breast cancerObjective: We had established a prediction model for predicting non-sentinel lymph node metastasis.The prediction of Model A above shows that the prediction performance is good,and external verification data is brought in to view the verification performance of this model.Methods: Data were collected from January 1,2008 to June 30,2019 from the Fourth Hospital of Hebei Medical University,and 156 patients were performed with SLNB concurrent lymphadenectomy.The data were brought into Model A.Results: The area under the ROC curve brought into the model A to be0.732.Conclusions: The model we created has good predictive power and can predict the rate of non-sentinel axillary lymph node metastasis.