A new pathological assessment method to assess residual lesions after neoadjuvant chemotherapy for breast cancer, residual disease in breast and nodes combined with Ki-67 (RDBN-K)

Ruoqi Han; Yanqi Ma; Zhikun Liu; Chunxiao Li; Sainan Li; Xiangshun Kong; Meng Han; Yueping Liu; Cuizhi Geng

doi:10.21037/tbcr-21-39

A new pathological assessment method to assess residual lesions after neoadjuvant chemotherapy for breast cancer, residual disease in breast and nodes combined with Ki-67 (RDBN-K)

Transl Breast Cancer Res. 2022 Jul 30:3:24. doi: 10.21037/tbcr-21-39. eCollection 2022.

Authors

Ruoqi Han¹, Yanqi Ma², Zhikun Liu³, Chunxiao Li¹, Sainan Li¹, Xiangshun Kong⁴, Meng Han⁵, Yueping Liu², Cuizhi Geng¹

Affiliations

¹ Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
² Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
³ Department of Radiotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
⁴ Department of Breast Surgery, Xingtai People's Hospital, Xingtai, China.
⁵ Department of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China.

Abstract

Background: The accurate assessment of residual tumor tissue after neoadjuvant chemotherapy (NAC) for breast cancer is closely related to the subsequent treatment and prognosis of patients. The objective of this study is to develop a new pathological assessment metric for breast cancer patients through combining residual disease in breast and nodes (RDBN) and the Ki-67 expression status after NAC. We call the new metric residual disease in breast and nodes combined with Ki-67 (RDBN-K) and aim to study its significance for prognosis.

Methods: A total of 723 breast cancer patients with TNM staging II to III who received NAC and surgical treatment underwent residual disease evaluation by RDBN-K and RDBN. All patients were followed up for a median of 44 months. We used pairwise stratified analysis to compare the accuracy and clinical significance of the RDBN and RDBN-K.

Results: Pairwise stratified analysis revealed that DFS and OS had larger difference between RDBN-K-3 and RDBN-K-4 compared to between RDBN-3 and RDBN-4. Moreover, RDBN-K also showed larger differences in OS between stage 2 and 3 compared to RDBN alone.

Conclusions: Incorporating Ki-67 expression status into RDBN improved the accuracy in residual tumor burden assessment after NAC. RDBN-K is a better metric for predicting treatment outcomes and identify patients who warrant follow-up intensive treatment.

Keywords: Ki-67; Residual disease in breast and nodes (RDBN); neoadjuvant chemotherapy (NAC); prognosis; residual disease in breast and nodes combined with Ki-67 (RDBN-K).