Neoadjuvant therapy is a common form of treatment in locally advanced breast cancer (LABC) patients. Besides some guidelines for grading regression, a standardized general scheme is not yet available. The aim of our study was to compare the prognostic impact of different regression grading systems, namely the TR/NR, Chevallier, Sataloff, Denkert-Sinn, Miller-Payne, NSABP-B18, Residual Disease in Breast and Nodes and Residual Cancer Burden (RCB) on disease-free (DFS) and overall survival (OS). Data of 746 breast cancer patients treated in neoadjuvant setting between 1999 and 2019 have been included. The different regression grades and follow-up data were collected from medical charts. Statistical analysis included the Kaplan-Meier method, log-rank test and multivariate Cox regression. The average patient age was 55 years. The DFS and OS estimates of patients with complete pathological regression and residual in situ carcinoma have been significantly more favorable than those having partial regression or no signs of regression (pDFS<0.001, pOS < 0.001). Significant differences were found between DFS estimates of classes with partial regression and without regression defined by RCB. Concerning DFS estimates, the RCB classification (p = 0.019), while regarding OS data the y-stage (p = 0.011) and the nodal status (ypN; p = 0.045) were significant prognosticators by multivariate Cox regression. Regression grading systems help the evaluation of regression in LABC patients treated with neoadjuvant therapy. Of the several grading systems compared, the RCB classification makes the best distinction between the outcomes of the different classes, therefore we recommend the inclusion of RCB into the histopathological findings.

新辅助治疗是局部晚期乳腺癌（LABC）患者的常见治疗形式。除了一些回归分级指南外，还没有标准化的通用方案。我们研究的目的是比较不同回归分级系统的预后影响，即 TR/NR、Chevallier、Sataloff、Denkert-Sinn、Miller-Payne、NSABP-B18、乳腺和淋巴结残留疾病以及残留癌症负担（ RCB）对无病生存（DFS）和总生存（OS）的影响。纳入了 1999 年至 2019 年间接受新辅助治疗的 746 名乳腺癌患者的数据。从医学图表中收集不同的回归等级和随访数据。统计分析包括Kaplan-Meier方法、对数秩检验和多元Cox回归。患者的平均年龄为 55 岁。病理完全消退和原位癌残留的患者的 DFS 和 OS 估计明显优于部分消退或无消退迹象的患者（pDFS<0.001，pOS <0.001）。在具有部分回归和不具有 RCB 定义的回归的类别的 DFS 估计之间发现了显着差异。关于 DFS 估计，RCB 分类 ( p  = 0.019)，而关于 OS 数据，y 分期 ( p  = 0.011) 和淋巴结状态 (ypN；p  = 0.045) 是多元 Cox 回归的重要预测因素。回归分级系统有助于评估接受新辅助治疗的 LABC 患者的回归情况。在比较的几个分级系统中，RCB 分类可以最好地区分不同类别的结果，因此我们建议将 RCB 纳入组织病理学结果中。