Evidence map›Paper›PMID 38751546›Full record

ArticleTranslational breast cancer research : a journal focusing on translational research in breast cancer2022

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

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Article in Translational breast cancer research : a journal focusing on translational research in breast cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Ruoqi HanDepartment of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Yanqi MaDepartment of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Zhikun LiuDepartment of Radiotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Chunxiao LiDepartment of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Sainan LiDepartment of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Xiangshun KongDepartment of Breast Surgery, Xingtai People's Hospital, Xingtai, China.
Meng HanDepartment of Breast Surgery, The First Hospital of Qinhuangdao, Qinhuangdao, China.
Yueping LiuDepartment of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Cuizhi GengDepartment of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Ki-67neoadjuvant chemotherapy (NAC)prognosisresidual disease in breast and nodes combined with Ki-67 (RDBN-K)Residual disease in breast and nodes (RDBN)

Identifiers

PMID38751546
PMCPMC11093006

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