Establishment and Verification of a Predictive Model for Node Pathological Complete Response After Neoadjuvant Chemotherapy for Initial Node Positive Early Breast Cancer - Zhu, Jiujun; Jiao, Dechuang; Yan, Min; Chen, Xiuchun; Wang, Chengzheng; Lu, Zhenduo; Li, Lianfang; Sun, Xianfu; Qin, Li; Guo, Xuhui; Zhang, Chongjian; Qiao, Jianghua; et al. (Frontiers in Oncology, 2021)
Objective Axillary node status after neoadjuvant chemotherapy (NCT) in early breast cancer patients influences the axillary surgical staging procedure. This study was conducted for the identification of the likelihood of patients being node pathological complete response (pCR) post NCT. We aimed to recognize patients most likely to benefit from sentinel lymph node biopsy (SLNB) following NCT and to reduce the risk of missed detection of positive lymph nodes through the construction and validation of a clinical preoperative scoring prediction model. Methods The existing data (from March 2010 to December 2018) of the Chinese Society of Clinical Oncology Breast Cancer Database (CSCO-BC) was used to evaluate the independent related factors of node pCR after NCT by Binary Logistic Regression analysis. A predictive model was established according to the score of considerable factors to identify ypN0. Model performance was confirmed in a cohort of NCT patients treated between January 2019 and December 2019 in Henan Cancer Hospital, and model discrimination was evaluated via assessing the area under the receiver operating characteristic (ROC) curve (AUC). Results Multivariate regression analysis showed that the node stage before chemotherapy, the expression level of Ki-67, biologic subtype, and breast pCR were all independent related factors of ypN0 after chemotherapy. According to the transformation and summation of odds ratio (OR) values of each variable, the scoring system model was constructed with a total score of 1-5. The AUC for the ROC curves was 0.715 and 0.770 for the training and the validation set accordingly. Conclusions A model was established and verified for predicting ypN0 after chemotherapy in newly diagnosed cN+ patients and the model had good accuracy and efficacy. The underlined effective model can suggest axillary surgical planning, and reduce the risk of missing positive lymph nodes by SLNB after NCT. It has great value for identifying initial cN+ patients who are more appropriate for SLNB post-chemotherapy.